Evolutionary algorithm
You are encouraged to solve this task according to the task description, using any language you may know.
Starting with:
- The
targetstring:"METHINKS IT IS LIKE A WEASEL". - An array of random characters chosen from the set of upper-case letters together with the space, and of the same length as the target string. (Call it the
parent). - A
fitnessfunction that computes the ‘closeness’ of its argument to the target string. - A
mutatefunction that given a string and a mutation rate returns a copy of the string, with some characters probably mutated. - While the
parentis not yet thetarget:
- copy the
parentC times, each time allowing some random probability that another character might be substituted usingmutate. - Assess the
fitnessof the parent and all the copies to thetargetand make the most fit string the newparent, discarding the others. - repeat until the parent converges, (hopefully), to the target.
- copy the
- See also
- Wikipedia entry: Weasel algorithm.
- Wikipedia entry: Evolutionary algorithm.
Note: to aid comparison, try and ensure the variables and functions mentioned in the task description appear in solutions
A cursory examination of a few of the solutions reveals that the instructions have not been followed rigorously in some solutions. Specifically,
- While the
parentis not yet thetarget:
- copy the
parentC times, each time allowing some random probability that another character might be substituted usingmutate.
- copy the
Note that some of the the solutions given retain characters in the mutated string that are correct in the target string. However, the instruction above does not state to retain any of the characters while performing the mutation. Although some may believe to do so is implied from the use of "converges"
(:* repeat until the parent converges, (hopefully), to the target.
Strictly speaking, the new parent should be selected from the new pool of mutations, and then the new parent used to generate the next set of mutations with parent characters getting retained only by not being mutated. It then becomes possible that the new set of mutations has no member that is fitter than the parent!
As illustration of this error, the code for 8th has the following remark.
Create a new string based on the TOS, changing randomly any characters which don't already match the target:
NOTE: this has been changed, the 8th version is completely random now
Clearly, this algo will be applying the mutation function only to the parent characters that don't match to the target characters!
To ensure that the new parent is never less fit than the prior parent, both the parent and all of the latest mutations are subjected to the fitness test to select the next parent.
11l
V target = Array(‘METHINKS IT IS LIKE A WEASEL’)
V alphabet = ‘ ABCDEFGHIJLKLMNOPQRSTUVWXYZ’
V p = 0.05
V c = 100
F neg_fitness(trial)
R sum(zip(trial, :target).map((t, h) -> Int(t != h)))
F mutate(parent)
R parent.map(ch -> (I random:() < :p {random:choice(:alphabet)} E ch))
V parent = (0 .< target.len).map(_ -> random:choice(:alphabet))
V i = 0
print((‘#3’.format(i))‘ ’parent.join(‘’))
L parent != target
V copies = ((0 .< c).map(_ -> mutate(:parent)))
parent = min(copies, key' x -> neg_fitness(x))
print((‘#3’.format(i))‘ ’parent.join(‘’))
i++- Output:
0 FGUQJPIPMOUSPW MRHJQMNLF GZP 0 FGURJPIPMOUSPW LRHJQMSLF GZP 1 FGURJPIPMOUSPW LRHJQASLF GLP 2 FGURJPIPMOUSPW LRHJQASWFYGLP 3 FGURZPKZMOUSPW LRHJQASWFYGLP 4 FJUHZPKZMOUSPW LRHJQASWFYGLP 5 FJUHZNKZMOUSPW LRHJQASWFYGLP 6 FJUHZNKZMOUSPW LRHJBASWFYGLP 7 FJUHZNKSMOUSPW LRHJBASWFYGLP 8 FJUHKNKSMOU PW LRHJBABWFYGLP 9 FJUHKNKSMOU PW LRHJBABWFYGLL 10 FJUHKNKSMOU PW LRKJBABWFYGLL ... 76 METHINKS IT IS LIKE A WEASEL
8080 Assembly
MRATE: equ 26 ; Chance of mutation (MRATE/256)
COPIES: equ 100 ; Amount of copies to make
fname1: equ 5Dh ; First filename on command line (used for RNG seed)
fname2: equ 6Dh ; Second filename (also used for RNG seed)
org 100h
;;; Make random seed from the two CP/M 'filenames'
lxi b,rnddat
lxi h,fname1
xra a
call seed ; First four bytes from fn1 make X
call seed ; Second four bytes from fn1 make A
mvi l,fname2
call seed ; First four bytes from fn2 make B
call seed ; Second four bytes from fn2 make C
;;; Create the first parent (random string)
lxi h,parent
mvi e,tgtsz-1
genchr: call rndchr ; Get random character
mov m,a ; Store it
inx h
dcr e
jnz genchr
mvi m,'$' ; CP/M string terminator
;;; Main loop
loop: lxi d,parent
push d
call puts ; Print current parent
pop h ; Calculate fitness
call fitnes
xra a ; If it is 0, all characters match,
ora d
rz ; So we stop.
lxi h,kids ; Otherwise, set HL to start of children,
mvi a,0FFh ; Initialize maximum fitness value
sta maxfit
mvi a,COPIES ; Initialize copy counter
sta copies
;;; Make copies
copy: push h ; Store the place where the copy will go
call mutcpy ; Make a mutated copy of the parent
pop h ; Get the place where the copy went
push h ; But keep it on the stack
call fitnes ; Calculate the fitness of that copy
pop h ; Get place where copy went
lda maxfit ; Get current best fitness
cmp d ; Compare to fitness of current copy
jc next ; If it wasn't better, next copy
shld maxptr ; If it was better, store pointer
mov a,d
sta maxfit ; And new max fitness
next: lxi b,tgtsz ; Get place for next copy
dad b
xchg ; Keep in DE
lxi h,copies
dcr m ; Any copies left to make?
xchg
jnz copy ; Then make another copy
lhld maxptr ; Otherwise, get location of copy with best fitness
lxi b,parent ; Make that the new parent
pcopy: mov a,m ; Get character from copy
inx h
stax b ; Store into location of parent
inx b
cpi '$' ; Check for string terminator
jnz pcopy ; If it isn't, copy next character
jmp loop ; Otherwise, mutate new parent
;;; Make a copy of the parent, mutate it, and store it at [HL].
mutcpy: lxi b,parent
mcloop: ldax b ; Get current character
inx b
mov m,a ; Write it to new location
inx h
cpi '$' ; Was it the string terminator?
rz ; Then stop
call rand ; Otherwise, get random value
cpi MRATE ; Should we mutate this character?
jnc mcloop ; If not, just copy next character
call rndchr ; Otherwise, get a random character
dcx h ; And store it instead of the character we had
mov m,a
inx h
jmp mcloop
;;; Calculate fitness of candidate under [HL], fitness is
;;; returned in D. Fitness is "inverted", i.e. a fitness of 0
;;; means everything matches.
fitnes: lxi b,target
lxi d,tgtsz ; E=counter, D=fitness
floop: dcr e ; Done yet?
rz ; If so, return.
ldax b ; Get target character
inx b
cmp m ; Compare to current character
inx h
jz floop ; If they match, don't do anything
inr d ; If they don't match, count this
jmp floop
;;; Generate a random uppercase letter, or a space
;;; Return in A, other registers preserved
rndchr: call rand ; Get a random value
ani 31 ; from 0 to 31
cpi 28 ; If 28 or higher,
jnc rndchr ; get another value
adi 'A' ; Make uppercase letter
cpi 'Z'+1 ; If the result is 'Z' or lower,
rc ; then return it,
mvi a,' ' ; otherwise return a space
ret
;;; Load 4 bytes from [HL] and use them, plus A, as part of seed
;;; in [BC]
seed: mvi e,4 ; 4 bytes
sloop: xra m
inx h
dcr e
jnz sloop
stax b
inx b
ret
;;; Random number generator using XABC algorithm
rand: push h
lxi h,rnddat
inr m ; X++
mov a,m ; X
inx h
xra m ; ^ C
inx h
xra m ; ^ A
mov m,a ; -> A
inx h
add m ; + B
mov m,a ; -> B
rar ; >>1 (ish)
dcx h
xra m ; ^ A
dcx h
add m ; + C
mov m,a ; -> C
pop h
ret
;;; Print string to console using CP/M, saving all registers
puts: push h
push d
push b
push psw
mvi c,9 ; CP/M print string
call 5
lxi d,nl ; Print a newline as well
mvi c,9
call 5
pop b
pop d
pop h
pop psw
ret
nl: db 13,10,'$'
target: db 'METHINKS IT IS LIKE A WEASEL$'
tgtsz: equ $-target
rnddat: ds 4 ; RNG state
copies: ds 1 ; Copies left to make
maxfit: ds 1 ; Best fitness seen
maxptr: ds 2 ; Pointer to copy with best fitness
parent: equ $ ; Store current parent here
kids: equ $+tgtsz ; Store mutated copies here- Output:
BEFHWYJNLPTBQDG KAULUCAHZC S BEFHWYJNZPTBQDG OAULUCOHAK S BEFHWYJNEPTMQDG OYULUCSHAKML HEFHWESNEPTMQDG IYULU SHAKML HETHWESMEPTMQDB IYULU SHAKML HETHWESMEPTLQEB IYU U XHAHML HETHWESMEPTLQEB IYE U XHAHML HETHWNSMEPTDQEB IYE U XHAPML HETHWNSSEPTKQAB IYE U XHAPML HEUHWNSSEPT IAW IKU U XHAPML HEUHWNSSEPT IAW IKU U OHAPML ... METHINKZ IT IS LRKE A WEASEL METHINKZ IT IS LIKE A WEASEL METHINKZ IT IS LIKE A WEASEL METHINKZ IT IS LIKE A WEASEL METHINKZ IT IS LIKE A WEASEL METHINKV IT IS LIKE A WEASEL METHINKV IT IS LIKE A WEASEL METHINKV IT IS LIKE A WEASEL METHINKV IT IS LIKE A WEASEL METHINKS IT IS LIKE A WEASEL
8086 Assembly
bits 16
cpu 8086
MRATE: equ 26 ; Mutation rate (MRATE/256)
COPIES: equ 100 ; Amount of copies to make
gettim: equ 02Ch ; MS-DOS get time function
pstr: equ 9 ; MS-DOS print string
section .text
org 100h
;;; Use MS-DOS time to set random seed
mov ah,gettim
int 21h
mov [rnddat],cx
mov [rnddat+2],dx
;;; Make first parent (random characters)
mov di,parent
mov cx,target.size-1
getchr: call rndchr ; Get random character
stosb ; Store in parent
loop getchr
mov al,'$' ; Write string terminator
stosb
;;; Main loop.
loop: mov bx,parent ; Print current parent
mov dx,bx
call puts
call fitnes ; Check fitness
test cl,cl ; If zero, we're done
jnz nxmut ; If not, do another mutation
ret ; Quit to DOS
nxmut: mov dl,0FFh ; DL = best fitness yet
mov di,kids ; Set DI to start of memory for children
mov ch,COPIES ; CH = amount of copies to make
xor bp,bp
;;; Make copy, mutate, and test fitness
copy: mov bx,di ; Let BX = where next copy will go
call mutcpy ; Make the copy (and adjust DI)
call fitnes ; Check fitness
cmp cl,dl ; Is it better than the previous best one?
ja next ; If not, just do the next one,
mov dl,cl ; Otherwise, this is now the best one
lea bp,[di-target.size] ; Store a pointer to it in BP
next: dec ch ; One copy less
jnz copy ; Make another copy if we need to
mov si,bp ; We're done, the best child becomes
mov di,parent ; the parent for the next generation
mov cx,target.size
rep movsb
jmp loop ; Next generation
;;; Make copy of parent, mutating as we go, and store at [DI]
mutcpy: mov si,parent
.loop: lodsb ; Get byte from parent
stosb ; Store in copy
cmp al,'$' ; Is it '$'?
je .out ; Then we're done
call rand ; Otherwise, should we mutate?
cmp al,MRATE
ja .loop ; If not, do next character
call rndchr ; But if so, get random character,
mov [di-1],al ; and overwrite the current character.
jmp .loop ; Then do the next character.
.out: ret
;;; Get fitness of character in [BX]
fitnes: mov si,target
xor cl,cl ; Fitness
.loop: lodsb ; Get target character
cmp al,'$' ; Done?
je .out ; Then stop
cmp al,[bx] ; Equal to character under [BX]?
lahf ; Keep flags
inc bx ; Increment BX
sahf ; Restore flags (was al=[bx]?)
je .loop ; If equal, do next character
inc cx ; Otherwise, count this as a mismatch
jmp .loop
.out: ret
;;; Generate random character, [A-Z] or space.
rndchr: call rand ; Get random number
and al,31 ; Lower five bits
cmp al,27 ; One of 27 characters?
jae rndchr ; If not, get new random number
add al,'A' ; Make uppercase letter
cmp al,'Z' ; More than 'Z'?
jbe .out ; If not, it's OK
mov al,' ' ; Otherwise, give a space
.out: ret
;;; Random number generator using XABC algorithm
;;; Returns random byte in AL
rand: push cx
push dx
mov cx,[rnddat] ; CH=X CL=A
mov dx,[rnddat+2] ; DH=B DL=C
inc ch ; X++
xor cl,ch ; A ^= X
xor cl,dl ; A ^= C
add dh,cl ; B += A
mov al,dh ; C' = B
shr al,1 ; C' >>= 1
xor al,cl ; C' ^= A
add al,dl ; C' += C
mov dl,al ; C = C'
mov [rnddat],cx
mov [rnddat+2],dx
pop dx
pop cx
ret
;;; Print string in DX, plus a newline, saving registers
puts: push ax
push dx
mov ah,pstr
int 21h
mov dx,nl
int 21h
pop dx
pop ax
ret
section .data
nl: db 13,10,'$'
target: db 'METHINKS IT IS LIKE A WEASEL$'
.size: equ $-target
section .bss
rnddat: resb 4 ; RNG state
parent: resb target.size ; Place to store current parent
kids: resb COPIES*target.size ; Place to store children
- Output:
HIIBZTIDHHEFZMUEDCXQRUH GNXK HIIBRTODHHE ZMUEDCXQAUHIONXK HECBRYCDHHE ZMUEBCXQAMHIONXK HECBRYCDYHE UMUEBCE AMHIONXK HECBRNCDYHE DMUEBCE AMHIONXK HECHRNCDYHE DMUEBCE AMHIONXK HECHRNKDYHE DMUEJCE AMHIOKXK HECHRNKDWHE DMUEJCE AMHIOKXK HELHRNKSWHE DMUEJCE AMHIOYXK HELHVNKSWHE DM EJRE ABHIAYXK ... METHINKS JT IS LIKU A WEASEL METHINKS JT IS LIKU A WEASEL METHINKS JT IS LIKU A WEASEL METHINKS IT IS LIKU A WEASEL METHINKS IT IS LIKU A WEASEL METHINKS IT IS LIKU A WEASEL METHINKS IT IS LIKU A WEASEL METHINKS IT IS LIKU A WEASEL METHINKS IT IS LIKU A WEASEL METHINKS IT IS LIKE A WEASEL
8th
\ RosettaCode challenge http://rosettacode.org/wiki/Evolutionary_algorithm
\ Responding to the criticism that the implementation was too directed, this
\ version does a completely random selection of chars to mutate
var gen
\ Convert a string of valid chars into an array of char-strings:
"ABCDEFGHIJKLMNOPQRSTUVWXYZ " null s:/ var, valid-chars
\ How many mutations each generation will handle; the larger, the slower each
\ generation but the fewer generations required:
300 var, #mutations
23 var, mutability
: get-random-char
valid-chars @
27 rand-pcg n:abs swap n:mod
a:@ nip ;
: mutate-string \ s -- s'
(
rand-pcg mutability @ n:mod not if
drop get-random-char
then
) s:map ;
: mutate \ s n -- a
\ iterate 'n' times over the initial string, mutating it each time
\ save the original string, as the best of the previous generation:
>r [] over a:push swap
(
tuck mutate-string
a:push swap
) r> times drop ;
\ compute Hamming distance of two strings:
: hamming \ s1 s2 -- n
0 >r
s:len n:1-
(
2 pick over s:@ nip
2 pick rot s:@ nip
n:- n:abs r> n:+ >r
) 0 rot loop
2drop r> ;
var best
: fitness-check \ s a -- s t
10000 >r
-1 best !
(
\ ix s ix s'
2 pick hamming
r@
over n:> if
rdrop >r
best !
else
2drop
then
)
a:each
rdrop best @ a:@ nip ;
: add-random-char \ s -- s'
get-random-char s:+ ;
\ take the target and make a random string of the same length
: initial-string \ s -- s
s:len "" swap
' add-random-char
swap times ;
: done? \ s1 s2 -- s1 s2 | bye
2dup s:= if
"Done in " . gen @ . " generations" . cr ;;;
then ;
: setup-random
rand rand rand-pcg-seed ;
: evolve
1 gen n:+!
\ create an array of #mutations strings mutated from the random string, drop the random
#mutations @ mutate
\ iterate over the array and pick the closest fit:
fitness-check
\ show this generation's best match:
dup . cr
\ check for end condition and continue if not done:
done? evolve ;
"METHINKS IT IS LIKE A WEASEL"
setup-random initial-string evolve bye
The output:
PIQSLOGHISTIPSDLZFGRDBYUCADA PIQSNOGH SQIPSDLZFGRDBYUEADA PIQSNOGH SQIPSDLZFG DBYUEDDA ... METHINKS IT IS LIKD A WEASEL METHINKS IT IS LIKD A WEASEL METHINKS IT IS LIKE A WEASEL Done in 43 generations
Ada
Very simple fitness determination. For testing purposes you can add a static seed value to the RNG initializations (sample output uses '12345' for both).
with Ada.Text_IO;
with Ada.Numerics.Discrete_Random;
with Ada.Numerics.Float_Random;
with Ada.Strings.Fixed;
with Ada.Strings.Maps;
procedure Evolution is
-- only upper case characters allowed, and space, which uses '@' in
-- internal representation (allowing subtype of Character).
subtype DNA_Char is Character range '@' .. 'Z';
-- DNA string is as long as target string.
subtype DNA_String is String (1 .. 28);
-- target string translated to DNA_Char string
Target : constant DNA_String := "METHINKS@IT@IS@LIKE@A@WEASEL";
-- calculate the 'closeness' to the target DNA.
-- it returns a number >= 0 that describes how many chars are correct.
-- can be improved much to make evolution better, but keep simple for
-- this example.
function Fitness (DNA : DNA_String) return Natural is
Result : Natural := 0;
begin
for Position in DNA'Range loop
if DNA (Position) = Target (Position) then
Result := Result + 1;
end if;
end loop;
return Result;
end Fitness;
-- output the DNA using the mapping
procedure Output_DNA (DNA : DNA_String; Prefix : String := "") is
use Ada.Strings.Maps;
Output_Map : Character_Mapping;
begin
Output_Map := To_Mapping
(From => To_Sequence (To_Set (('@'))),
To => To_Sequence (To_Set ((' '))));
Ada.Text_IO.Put (Prefix);
Ada.Text_IO.Put (Ada.Strings.Fixed.Translate (DNA, Output_Map));
Ada.Text_IO.Put_Line (", fitness: " & Integer'Image (Fitness (DNA)));
end Output_DNA;
-- DNA_Char is a discrete type, use Ada RNG
package Random_Char is new Ada.Numerics.Discrete_Random (DNA_Char);
DNA_Generator : Random_Char.Generator;
-- need generator for floating type, too
Float_Generator : Ada.Numerics.Float_Random.Generator;
-- returns a mutated copy of the parent, applying the given mutation rate
function Mutate (Parent : DNA_String;
Mutation_Rate : Float)
return DNA_String
is
Result : DNA_String := Parent;
begin
for Position in Result'Range loop
if Ada.Numerics.Float_Random.Random (Float_Generator) <= Mutation_Rate
then
Result (Position) := Random_Char.Random (DNA_Generator);
end if;
end loop;
return Result;
end Mutate;
-- genetic algorithm to evolve the string
-- could be made a function returning the final string
procedure Evolve (Child_Count : Positive := 100;
Mutation_Rate : Float := 0.2)
is
type Child_Array is array (1 .. Child_Count) of DNA_String;
-- determine the fittest of the candidates
function Fittest (Candidates : Child_Array) return DNA_String is
The_Fittest : DNA_String := Candidates (1);
begin
for Candidate in Candidates'Range loop
if Fitness (Candidates (Candidate)) > Fitness (The_Fittest)
then
The_Fittest := Candidates (Candidate);
end if;
end loop;
return The_Fittest;
end Fittest;
Parent, Next_Parent : DNA_String;
Children : Child_Array;
Loop_Counter : Positive := 1;
begin
-- initialize Parent
for Position in Parent'Range loop
Parent (Position) := Random_Char.Random (DNA_Generator);
end loop;
Output_DNA (Parent, "First: ");
while Parent /= Target loop
-- mutation loop
for Child in Children'Range loop
Children (Child) := Mutate (Parent, Mutation_Rate);
end loop;
Next_Parent := Fittest (Children);
-- don't allow weaker children as the parent
if Fitness (Next_Parent) > Fitness (Parent) then
Parent := Next_Parent;
end if;
-- output every 20th generation
if Loop_Counter mod 20 = 0 then
Output_DNA (Parent, Integer'Image (Loop_Counter) & ": ");
end if;
Loop_Counter := Loop_Counter + 1;
end loop;
Output_DNA (Parent, "Final (" & Integer'Image (Loop_Counter) & "): ");
end Evolve;
begin
-- initialize the random number generators
Random_Char.Reset (DNA_Generator);
Ada.Numerics.Float_Random.Reset (Float_Generator);
-- evolve!
Evolve;
end Evolution;
sample output:
First: FCLYNZAOQ KBSZHJAKAWOSZKBOBT, fitness: 1 20: MKTHCPKS IT MSBBIKEVB SPASEH, fitness: 17 40: METHIDKS IT NS BIKE B OQASET, fitness: 21 60: METHIDKS IT NS BIKE B OQASET, fitness: 21 80: METHIDKS IT NS BIKE B OQASET, fitness: 21 100: METHIDKS IT VS BIKE B WQASEP, fitness: 22 120: METHIDKS IT VS BIKE B WQASEP, fitness: 22 140: METHIDKS ITBVS LIKE B WEASEP, fitness: 23 160: METHIDKS ITBVS LIKE B WEASEP, fitness: 23 180: METHIDKS ITBVS LIKE B WEASEP, fitness: 23 200: METHIDKS ITBIS LIKE B WEASEP, fitness: 24 220: METHITKS ITBIS LIKE B WEASEL, fitness: 25 240: METHITKS ITBIS LIKE B WEASEL, fitness: 25 260: METHITKS ITBIS LIKE B WEASEL, fitness: 25 280: METHITKS ITBIS LIKE B WEASEL, fitness: 25 300: METHITKS ITBIS LIKE B WEASEL, fitness: 25 320: METHITKS ITBIS LIKE B WEASEL, fitness: 25 340: METHITKS ITBIS LIKE B WEASEL, fitness: 25 360: METHITKS ITBIS LIKE B WEASEL, fitness: 25 380: METHINKS ITBIS LIKE A WEASEL, fitness: 27 Final ( 384): METHINKS IT IS LIKE A WEASEL, fitness: 28
ABC
PUT "ABCDEFGHIJKLMNOPQRSTUVWXYZ " IN alphabet
HOW TO RETURN initial.state target:
SHARE alphabet
PUT "" IN state
FOR c IN target: PUT state^choice alphabet IN state
RETURN state
HOW TO RETURN state fitness target:
PUT #target IN score
FOR i IN {1..#target}:
IF state item i = target item i: PUT score-1 IN score
RETURN score
HOW TO RETURN chance mutate state:
SHARE alphabet
PUT "" IN mutated
FOR i IN {1..#state}:
SELECT:
random < chance: PUT choice alphabet IN next
ELSE: PUT state item i IN next
PUT mutated^next IN mutated
RETURN mutated
HOW TO EVOLVE TOWARD target:
PUT 0.1 IN mutation.rate
PUT 100 IN generation.size
PUT initial.state target IN state
WHILE state fitness target > 0:
WRITE (state fitness target)>>2, ": ", state/
PUT {} IN next.generation
FOR i IN {1..generation.size}:
PUT mutation.rate mutate state IN child
PUT child fitness target IN score
PUT child IN next.generation[score]
PUT next.generation[min keys next.generation] IN state
WRITE (state fitness target)>>2, ": ", state/
EVOLVE TOWARD "METHINKS IT IS LIKE A WEASEL"- Output:
27: CYPPYRQHMACPLQIZLPETRJVVPYLI 26: CYPPYRQHMICPLQIZLPEDRJVVPILI 25: CYKPYRQH ICPLWIZLPEDRJVJPILI 24: CWKPWWQH ICPLSIZLPEDRJVJPILI 23: CWKPWWQH ICPLSIZLPEDR BJPILI 21: CWKPWWQH ICPLSIZLSEDA BJA LI 20: JWKPWWKH ICPLSIZLSEDA BJA LK 19: WWKPIWKG ICPLSIZLSEDA BJA LK 18: WWKPIWKG ICPLSILLSEDA BJA LK 17: WWKPIWKG ICPLSILISEDA BJA LK 17: IWMPIWKG ICPLSILISEDA BJA LK 16: IWMPIWKG ICPLSILISEDA WJA LK 15: IWMPIWKG ICPLSILISEDA WEA LK ... 1: METHINKS IT IS LIKE A WEAS L 1: METHINKS IT IS LIKE A WEAS L 1: METHINKS IT IS LIKE A WEAS L 0: METHINKS IT IS LIKE A WEASEL
Aime
integer
fitness(data t, data b)
{
integer c, f, i;
f = 0;
for (i, c in b) {
f += sign(t[i] ^ c);
}
f;
}
void
mutate(data e, data b, data u)
{
integer c;
for (, c in b) {
e.append(drand(15) ? c : u[drand(26)]);
}
}
integer
main(void)
{
data b, t, u;
integer f, i;
t = "METHINK IT IS LIKE A WEASEL";
u = "ABCDEFGHIJKLMNOPQRSTUVWXYZ ";
i = ~t;
while (i) {
i -= 1;
b.append(u[drand(26)]);
}
f = fitness(t, b);
while (f) {
data n;
integer a;
o_form("/lw4/~\n", f, b);
n = b;
i = 32;
while (i) {
data c;
i -= 1;
mutate(c, b, u);
a = fitness(t, c);
if (a < f) {
f = a;
n = c;
}
}
b = n;
}
o_form("/lw4/~\n", f, b);
return 0;
}- Output:
23 EAAXIZJROVOHSKREBNSAFHEKF B 22 EAUHIZJREVOHSKREBNSAFHEKF B 21 IAUHIZJREVOHSKREBESAFHEKF B 20 IKUHIZJRETOTSKREBESAFHEKFWB 20 IKUHIZJRETOTSKREBESAFHEKFWB 19 IKUHIZJRET USKREBESAFHEKFWA 19 IKUHIZJRET USKREBESAFHEKFWA 19 IKUHIZJRET USKREBESAFHEKFWA 18 IKUHIZJRET US REBESAFHEKFWA 18 IKUHIZJRET US REBESAFHEKFWA 17 IKMHIZJKET US REBESA HEKFWA 16 IKMHIZJKET US LEBEJA HEKJWA 16 IKMHIZJKET US LEBEJA HEKJWA 16 IKMHIZJKET US LEBEJA HEKJWA 16 IKMHIZJKET US LEBEJA HEKJWA 15 MKKHIZJ ET US LEBEJF HEKJWA 14 MEEHIZJ ET US LEBEJF HEKJWA 14 MEEHIZJ ET US LEBEJF HEKJWA 13 MEEHIZJ ET US LKBE F OEKJWA 12 MEEHIZJ ET US LKKE F OEKJWA 12 MEEHIZJ ET US LKKE F OEKJWA 11 MEEHIZJ ET US LIKE F OEKJWA 11 MEEHIZJ ET US LIKE F OEKJWA 10 MEEHIZJ IT US LIKE F OEKJWA 10 MEEHIZJ IT US LIKE F OEKJWA ... 1 METHINK IT IS LIKE F WEASEL 1 METHINK IT IS LIKE F WEASEL 0 METHINK IT IS LIKE A WEASEL
ALGOL 68
Note: This specimen retains the original C coding style.
STRING target := "METHINKS IT IS LIKE A WEASEL";
PROC fitness = (STRING tstrg)REAL:
(
INT sum := 0;
FOR i FROM LWB tstrg TO UPB tstrg DO
sum +:= ABS(ABS target[i] - ABS tstrg[i])
OD;
# fitness := # 100.0*exp(-sum/10.0)
);
PROC rand char = CHAR:
(
#STATIC# []CHAR ucchars = "ABCDEFGHIJKLMNOPQRSTUVWXYZ ";
# rand char := # ucchars[ENTIER (random*UPB ucchars)+1]
);
PROC mutate = (REF STRING kid, parent, REAL mutate rate)VOID:
(
FOR i FROM LWB parent TO UPB parent DO
kid[i] := IF random < mutate rate THEN rand char ELSE parent[i] FI
OD
);
PROC kewe = ( STRING parent, INT iters, REAL fits, REAL mrate)VOID:
(
printf(($"#"4d" fitness: "g(-6,2)"% "g(-6,4)" '"g"'"l$, iters, fits, mrate, parent))
);
PROC evolve = VOID:
(
FLEX[UPB target]CHAR parent;
REAL fits;
[100]FLEX[UPB target]CHAR kid;
INT iters := 0;
kid[LWB kid] := LOC[UPB target]CHAR;
REAL mutate rate = 0.05;
# initialize #
FOR i FROM LWB parent TO UPB parent DO
parent[i] := rand char
OD;
fits := fitness(parent);
WHILE
INT j;
REAL kf;
FOR j FROM LWB kid TO UPB kid DO
mutate(kid[j], parent, mutate rate)
OD;
FOR j FROM LWB kid TO UPB kid DO
kf := fitness(kid[j]);
IF fits < kf THEN
fits := kf;
parent := kid[j]
FI
OD;
fits < 100.0
DO
kewe( parent, iters, fits, mutate rate );
iters+:=1
OD;
kewe( parent, iters, fits, mutate rate )
);
main:
(
evolve
)Sample output:
#0000 fitness: 0.00% 0.0500 'QBDVGDQUELSIJULMHYUGD MPYMUX' #0001 fitness: 0.00% 0.0500 'QBJQGDQUELSIJULMHYOGD MPYMUP' #0002 fitness: 0.00% 0.0500 'QBJQGDQUELSIJP MHYOGD MPYMUQ' #0003 fitness: 0.00% 0.0500 'QBJQGFQUELSIJP MHYO D MPYMUQ' #0004 fitness: 0.00% 0.0500 'QBJQGFQU LSIJP MHOO D MPYMUQ' #0005 fitness: 0.00% 0.0500 'QBJQGSQU LSIJP MHOO D MPYMFN' #0006 fitness: 0.00% 0.0500 'QBJQGSQU LSIJP MHOO D MPCMFN' #0007 fitness: 0.00% 0.0500 'QBRQGSQU LSIJP MHOO D MPCMFN' #0008 fitness: 0.00% 0.0500 'QBRQGLQU LSIJP MHOE D MPCMFN' ... #0097 fitness: 90.48% 0.0500 'METHIOKS IT IS LIKE A WEASEL' #0098 fitness: 90.48% 0.0500 'METHIOKS IT IS LIKE A WEASEL' #0099 fitness: 90.48% 0.0500 'METHIOKS IT IS LIKE A WEASEL' #0100 fitness: 90.48% 0.0500 'METHIOKS IT IS LIKE A WEASEL' #0101 fitness: 90.48% 0.0500 'METHIOKS IT IS LIKE A WEASEL' #0102 fitness: 90.48% 0.0500 'METHIOKS IT IS LIKE A WEASEL' #0103 fitness: 90.48% 0.0500 'METHIOKS IT IS LIKE A WEASEL' #0104 fitness: 100.00% 0.0500 'METHINKS IT IS LIKE A WEASEL'
Amstrad CPC Locomotive BASIC
Finding the target string on an Amstrad CPC takes a bit less than 45 minutes.
10 s$="METHINKS IT IS LIKE A WEASEL"
20 cc=100
30 prop=0.05
40 sl=len(s$)
50 dim c(cc,sl)
60 dim o(sl)
70 dim v(cc)
80 cp = 1
90 start=time
100 for i=1 to sl
110 o(i)=asc(mid$(s$,i,1))
120 if o(i)=32 then o(i)=64
130 next
140 for j=1 to sl:c(1,j)=int(64+27*rnd(1)):next
150 for i=2 to cc
160 for j=1 to sl:c(i,j)=c(cp,j):next
170 next
180 for i=1 to cc
190 for j=1 to sl
200 if rnd(1)>prop then goto 220
210 c(i,j)=int(64+27*rnd(1))
220 next j: next i
230 sc=0:bsi=1
240 for i=1 to cc
250 v(i)=0
260 for j=1 to sl
270 if c(i,j)=o(j) then v(i)=v(i)+1
280 next j
290 if v(i)>sc then sc=v(i):bsi=i
300 next i
310 print sc"@"bsi;:if bsi<10 then print " ";
320 for i=1 to sl:?chr$(c(bsi, i)+(c(bsi, i)=64)*32);:next:?
330 if sc=sl then print "We have a weasel!":? "Time: "(time-start)/300:end
340 cp=bsi
350 goto 150
Output during calculation (excerpt):
27 @ 10 METHINKGS IT IS LQKE A WEASEL 27 @ 9 METHINKGS IT IS LYKE A WEASEL 27 @ 2 METHINKGS IT IS LYKE A WEASEL 27 @ 2 METHINKGS IT IS LYKE A WEASEL 27 @ 7 METHINKGS IT IS LYKE A WEASEL 27 @ 2 METHINKGS IT IS LYKE A WEASEL 28 @ 93 METHINKGS IT IS LIKE A WEASEL We have a weasel! Time: 2572.87
APL
evolve←{
⍺←0.1
target←'METHINKS IT IS LIKE A WEASEL'
charset←27↑⎕A
fitness←{target+.=⍵}
mutate←⍺∘{
(⍺>?(⍴target)/0){
⍺:(?⍴charset)⊃charset
⍵
}¨⍵
}
⍵{
target≡⎕←⍵:⍵
next←mutate¨⍺/⊂⍵
⍺∇(⊃⍒fitness¨next)⊃next
}charset[?(⍴target)/⍴charset]
}
- Output:
evolve 100 JDBBTX JVAWZVJUUGACMOMZEATQH JEBITX JVAWZVJUUGACMOMWEATQH JEBGTX JVAWZVJ UGACMORWEATQH JEHGTX JVAW VJ UGACMOJWEATQH JEHGTX JVAW VJ UGACMAJWEATQH IEHGTX JFAR VJ EGACMAJWEATEH IEHGTX JFAR VJ LGACMAJWEATEH IEHGTX FKAR IJ LGACMAJWEATEH IEJGTX FKAT IJ LGACMAJWEATEW MEJGTX FKAT IJ LGACKAJWEATEW MERGTX F AT IJ LGACKAJWEATEW ... METHI KS IT IS LIKE A WEASEL METHINKS IT IS LIKE A WPASEL METHINKS IT IS LIKE A WPASEL METHINKS IT IS LIKE A WPASEL METHINKS IT IS LIKE A WPASEL METHINKS IT IS LIKE A WPASEL METHINKS IT IS LIKE A WPASEL METHINKS IT IS LIKE A WPASEL METHINKS IT IS LIKE A WJASEL METHINKS IT IS LIKE A WEASEL METHINKS IT IS LIKE A WEASEL
Arturo
target: "METHINKS IT IS LIKE A WEASEL"
alphabet: [` `] ++ @`A`..`Z`
p: 0.05
c: 100
negFitness: function [trial][
result: 0
loop 0..dec size trial 'i ->
if target\[i] <> trial\[i] -> inc 'result
return result
]
mutate: function [parent][
result: ""
loop parent 'c ->
'result ++ (p > random 0.0 1.0)? -> sample alphabet -> c
return result
]
parent: ""
do.times: size target ->
'parent ++ sample alphabet
j: 0
copies: []
while [parent <> target][
'copies ++ map c 'i -> mutate parent
best: first copies
loop 1..dec size copies 'i [
if (negFitness copies\[i]) < negFitness best ->
best: copies\[i]
]
parent: best
print [pad to :string j 2 parent]
inc 'j
]
- Output:
0 JBTBAFNWA YYZTOUBPIUUSPTFKYK 1 JBTBAFNWA YYFTOUBPIUU PTFKYK 2 MBTBAFNWA YYFTOUBPDUU PTFKYK 3 MBTBCMNWA YYFTOUBPDUU PTFKEK 4 MBTBCMNWA YDFSOUBPDUU PTFKEK ... 27 METHINVSV TDIS LIKE A WZASEK 28 METHINVSV TDIS LIKE A WZASEL 29 METHINVSV TDIS LIKE A WZASEL 30 METHINVSV TDIS LIKE A WZASEL 31 METHINVSV TDIS LIKE A WZASEL 32 METHINVSV TDIS LIKE A WZASEL 33 METHINVSV TDIS LIKE A WZASEL 34 METHINVSV TDIS LIKE A WZASEL 35 METHINVS TMIS LIKE A WZASEL ... 57 METHINVS ITMIS LIKE A WEASEL 58 METHINVS IT IS LIKE A WEASEL 59 METHINVS IT IS LIKE A WEASEL 60 METHINKS IT IS LIKE A WEASEL
AutoHotkey
output := ""
target := "METHINKS IT IS LIKE A WEASEL"
targetLen := StrLen(target)
Loop, 26
possibilities_%A_Index% := Chr(A_Index+64) ; A-Z
possibilities_27 := " "
C := 100
parent := ""
Loop, %targetLen%
{
Random, randomNum, 1, 27
parent .= possibilities_%randomNum%
}
Loop,
{
If (target = parent)
Break
If (Mod(A_Index,10) = 0)
output .= A_Index ": " parent ", fitness: " fitness(parent, target) "`n"
bestFit := 0
Loop, %C%
If ((fitness := fitness(spawn := mutate(parent), target)) > bestFit)
bestSpawn := spawn , bestFit := fitness
parent := bestFit > fitness(parent, target) ? bestSpawn : parent
iter := A_Index
}
output .= parent ", " iter
MsgBox, % output
ExitApp
mutate(parent) {
local output, replaceChar, newChar
output := ""
Loop, %targetLen%
{
Random, replaceChar, 0, 9
If (replaceChar != 0)
output .= SubStr(parent, A_Index, 1)
else
{
Random, newChar, 1, 27
output .= possibilities_%newChar%
}
}
Return output
}
fitness(string, target) {
totalFit := 0
Loop, % StrLen(string)
If (SubStr(string, A_Index, 1) = SubStr(target, A_Index, 1))
totalFit++
Return totalFit
}
Output:
10: DETRNNKR IAQPFLNVKZ AMXEASEL, fitness: 14 20: METKNNKS IL PALLKKE A XEASEL, fitness: 20 30: METHGNKS IT PSXLKKE A XEASEL, fitness: 23 40: METHGNKS IT IS LKKE A EEASEL, fitness: 25 50: METHGNKS IT IS LKKE A WEASEL, fitness: 26 60: METHGNKS IT IS LKKE A WEASEL, fitness: 26 70: METHGNKS IT IS LIKE A WEASEL, fitness: 27 METHINKS IT IS LIKE A WEASEL, 72
AWK
I apply the rate to each character in each generated child. The number of generations required seems to be really sensitive to the rate. I used the default seeding in GNU awk to obtain the results below. I suspect the algorithm used to generate the pseudo-random numbers may also influence the rapidity of convergence but I haven't investigated that yet. The output shown was obtained using GNU Awk 3.1.5. BusyBox v1.20.0.git also works but using the same rate generates 88 generations before converging.
#!/bin/awk -f
function randchar(){
return substr(charset,randint(length(charset)+1),1)
}
function mutate(gene,rate ,l,newgene){
newgene = ""
for (l=1; l < 1+length(gene); l++){
if (rand() < rate)
newgene = newgene randchar()
else
newgene = newgene substr(gene,l,1)
}
return newgene
}
function fitness(gene,target ,k,fit){
fit = 0
for (k=1;k<1+length(gene);k++){
if (substr(gene,k,1) == substr(target,k,1)) fit = fit + 1
}
return fit
}
function randint(n){
return int(n * rand())
}
function evolve(){
maxfit = fitness(parent,target)
oldfit = maxfit
maxj = 0
for (j=1; j < D; j++){
child[j] = mutate(parent,mutrate)
fit[j] = fitness(child[j],target)
if (fit[j] > maxfit) {
maxfit = fit[j]
maxj = j
}
}
if (maxfit > oldfit) parent = child[maxj]
}
BEGIN{
target = "METHINKS IT IS LIKE A WEASEL"
charset = " ABCDEFGHIJKLMNOPQRSTUVWXYZ"
mutrate = 0.10
if (ARGC > 1) mutrate = ARGV[1]
lenset = length(charset)
C = 100
D = C + 1
parent = ""
for (j=1; j < length(target)+1; j++) {
parent = parent randchar()
}
print "target: " target
print "fitness of target: " fitness(target,target)
print "initial parent: " parent
gens = 0
while (parent != target){
evolve()
gens = gens + 1
if (gens % 10 == 0) print "after " gens " generations,","new parent: " parent," with fitness: " fitness(parent,target)
}
print "after " gens " generations,"," evolved parent: " parent
}
Output:
# ./awkevolution .08998 target: METHINKS IT IS LIKE A WEASEL fitness of target: 28 initial parent: EGVCODUCLCILXFXEPNHAMNV BP S after 10 generations, new parent: EGTSIDKS IT XFXXIKHAANUDEW S with fitness: 11 after 20 generations, new parent: MKTIIDKS IT IF XIKB A WEEWEL with fitness: 20 after 30 generations, new parent: M TIIDKS IT IF LIKE A WENSEL with fitness: 23 after 40 generations, new parent: METIIDKS IT IF LIKE A WEASEL with fitness: 25 after 50 generations, new parent: METHIDKS IT IS LIKE A WEASEL with fitness: 27 after 60 generations, new parent: METHINKS IT IS LIKE A WEASEL with fitness: 28 after 60 generations, evolved parent: METHINKS IT IS LIKE A WEASEL #
BASIC
BBC BASIC
target$ = "METHINKS IT IS LIKE A WEASEL"
parent$ = "IU RFSGJABGOLYWF XSMFXNIABKT"
mutation_rate = 0.5
children% = 10
DIM child$(children%)
REPEAT
bestfitness = 0
bestindex% = 0
FOR index% = 1 TO children%
child$(index%) = FNmutate(parent$, mutation_rate)
fitness = FNfitness(target$, child$(index%))
IF fitness > bestfitness THEN
bestfitness = fitness
bestindex% = index%
ENDIF
NEXT index%
parent$ = child$(bestindex%)
PRINT parent$
UNTIL parent$ = target$
END
DEF FNfitness(text$, ref$)
LOCAL I%, F%
FOR I% = 1 TO LEN(text$)
IF MID$(text$, I%, 1) = MID$(ref$, I%, 1) THEN F% += 1
NEXT
= F% / LEN(text$)
DEF FNmutate(text$, rate)
LOCAL C%
IF rate > RND(1) THEN
C% = 63+RND(27)
IF C% = 64 C% = 32
MID$(text$, RND(LEN(text$)), 1) = CHR$(C%)
ENDIF
= text$
Batch File
@echo off
setlocal enabledelayedexpansion
set target=M E T H I N K S @ I T @ I S @ L I K E @ A @ W E A S E L
set chars=A B C D E F G H I J K L M N O P Q R S T U V W X Y Z @
set tempcount=0
for %%i in (%target%) do (
set /a tempcount+=1
set target!tempcount!=%%i
)
call:parent
echo %target%
echo --------------------------------------------------------
:loop
call:fitness parent
set currentfit=%errorlevel%
if %currentfit%==28 goto end
echo %parent% - %currentfit% [%attempts%]
set attempts=0
:innerloop
set /a attempts+=1
title Attemps - %attempts%
call:mutate %parent%
call:fitness tempparent
set newfit=%errorlevel%
if %newfit% gtr %currentfit% (
set tempcount=0
set "parent="
for %%i in (%tempparent%) do (
set /a tempcount+=1
set parent!tempcount!=%%i
set parent=!parent! %%i
)
goto loop
)
goto innerloop
:end
echo %parent% - %currentfit% [%attempts%]
echo Done.
exit /b
:parent
set "parent="
for /l %%i in (1,1,28) do (
set /a charchosen=!random! %% 27 + 1
set tempcount=0
for %%j in (%chars%) do (
set /a tempcount+=1
if !charchosen!==!tempcount! (
set parent%%i=%%j
set parent=!parent! %%j
)
)
)
exit /b
:fitness
set fitness=0
set array=%1
for /l %%i in (1,1,28) do if !%array%%%i!==!target%%i! set /a fitness+=1
exit /b %fitness%
:mutate
set tempcount=0
set returnarray=tempparent
set "%returnarray%="
for %%i in (%*) do (
set /a tempcount+=1
set %returnarray%!tempcount!=%%i
set %returnarray%=!%returnarray%! %%i
)
set /a tomutate=%random% %% 28 + 1
set /a mutateto=%random% %% 27 + 1
set tempcount=0
for %%i in (%chars%) do (
set /a tempcount+=1
if %mutateto%==!tempcount! (
set %returnarray%!tomutate!=%%i
)
)
set "%returnarray%="
for /l %%i in (1,1,28) do set %returnarray%=!%returnarray%! !%returnarray%%%i!
exit /b
- Output:
Sample Output:
M E T H I N K S @ I T @ I S @ L I K E @ A @ W E A S E L -------------------------------------------------------- R S T L U M F Q Y B T L G P L Q T B F C B X F S X S H Y - 3 [] R S T L I M F Q Y B T L G P L Q T B F C B X F S X S H Y - 4 [9] R S T L I M F Q Y B T L G S L Q T B F C B X F S X S H Y - 5 [49] R E T L I M F Q Y B T L G S L Q T B F C B X F S X S H Y - 6 [2] R E T L I M F Q Y B T L G S L Q T B F C B X F S X S H L - 7 [18] R E T L I M F Q Y B T L G S L Q T B F C B X W S X S H L - 8 [5] R E T L I M F Q Y B T @ G S L Q T B F C B X W S X S H L - 9 [13] R E T L I M F Q Y B T @ G S L L T B F C B X W S X S H L - 10 [114] R E T L I M K Q Y B T @ G S L L T B F C B X W S X S H L - 11 [9] R E T L I M K Q Y B T @ G S @ L T B F C B X W S X S H L - 12 [17] R E T L I M K S Y B T @ G S @ L T B F C B X W S X S H L - 13 [53] R E T L I M K S Y I T @ G S @ L T B F C B X W S X S H L - 14 [20] R E T L I M K S @ I T @ G S @ L T B F C B X W S X S H L - 15 [121] R E T L I M K S @ I T @ G S @ L T B F C B X W S X S E L - 16 [86] R E T L I M K S @ I T @ G S @ L T B F C B X W E X S E L - 17 [115] R E T H I M K S @ I T @ G S @ L T B F C B X W E X S E L - 18 [54] R E T H I M K S @ I T @ G S @ L T B F @ B X W E X S E L - 19 [121] R E T H I M K S @ I T @ G S @ L T B F @ B X W E A S E L - 20 [207] M E T H I M K S @ I T @ G S @ L T B F @ B X W E A S E L - 21 [5] M E T H I M K S @ I T @ G S @ L I B F @ B X W E A S E L - 22 [163] M E T H I M K S @ I T @ G S @ L I B E @ B X W E A S E L - 23 [84] M E T H I M K S @ I T @ G S @ L I K E @ B X W E A S E L - 24 [31] M E T H I N K S @ I T @ G S @ L I K E @ B X W E A S E L - 25 [432] M E T H I N K S @ I T @ I S @ L I K E @ B X W E A S E L - 26 [85] M E T H I N K S @ I T @ I S @ L I K E @ A X W E A S E L - 27 [144] M E T H I N K S @ I T @ I S @ L I K E @ A @ W E A S E L - 28 [227] Done.
C
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
const char target[] = "METHINKS IT IS LIKE A WEASEL";
const char tbl[] = "ABCDEFGHIJKLMNOPQRSTUVWXYZ ";
#define CHOICE (sizeof(tbl) - 1)
#define MUTATE 15
#define COPIES 30
/* returns random integer from 0 to n - 1 */
int irand(int n)
{
int r, rand_max = RAND_MAX - (RAND_MAX % n);
while((r = rand()) >= rand_max);
return r / (rand_max / n);
}
/* number of different chars between a and b */
int unfitness(const char *a, const char *b)
{
int i, sum = 0;
for (i = 0; a[i]; i++)
sum += (a[i] != b[i]);
return sum;
}
/* each char of b has 1/MUTATE chance of differing from a */
void mutate(const char *a, char *b)
{
int i;
for (i = 0; a[i]; i++)
b[i] = irand(MUTATE) ? a[i] : tbl[irand(CHOICE)];
b[i] = '\0';
}
int main()
{
int i, best_i, unfit, best, iters = 0;
char specimen[COPIES][sizeof(target) / sizeof(char)];
/* init rand string */
for (i = 0; target[i]; i++)
specimen[0][i] = tbl[irand(CHOICE)];
specimen[0][i] = 0;
do {
for (i = 1; i < COPIES; i++)
mutate(specimen[0], specimen[i]);
/* find best fitting string */
for (best_i = i = 0; i < COPIES; i++) {
unfit = unfitness(target, specimen[i]);
if(unfit < best || !i) {
best = unfit;
best_i = i;
}
}
if (best_i) strcpy(specimen[0], specimen[best_i]);
printf("iter %d, score %d: %s\n", iters++, best, specimen[0]);
} while (best);
return 0;
}
output
iter 0, score 26: WKVVYFJUHOMQJNZYRTEQAGDVXKYC
iter 1, score 25: WKVVTFJUHOMQJN YRTEQAGDVSKXC
iter 2, score 25: WKVVTFJUHOMQJN YRTEQAGDVSKXC
iter 3, score 24: WKVVTFJUHOMQJN YRTEQAGDVAKFC
...
iter 221, score 1: METHINKSHIT IS LIKE A WEASEL
iter 222, score 1: METHINKSHIT IS LIKE A WEASEL
iter 223, score 0: METHINKS IT IS LIKE A WEASEL
C#
using System;
using System.Collections.Generic;
using System.Linq;
static class Program {
static Random Rng = new Random((int)DateTime.Now.Ticks);
static char NextCharacter(this Random self) {
const string AllowedChars = " ABCDEFGHIJKLMNOPQRSTUVWXYZ";
return AllowedChars[self.Next() % AllowedChars.Length];
}
static string NextString(this Random self, int length) {
return String.Join("", Enumerable.Repeat(' ', length)
.Select(c => Rng.NextCharacter()));
}
static int Fitness(string target, string current) {
return target.Zip(current, (a, b) => a == b ? 1 : 0).Sum();
}
static string Mutate(string current, double rate) {
return String.Join("", from c in current
select Rng.NextDouble() <= rate ? Rng.NextCharacter() : c);
}
static void Main(string[] args) {
const string target = "METHINKS IT IS LIKE A WEASEL";
const int C = 100;
const double P = 0.05;
// Start with a random string the same length as the target.
string parent = Rng.NextString(target.Length);
Console.WriteLine("START: {0,20} fitness: {1}",
parent, Fitness(target, parent));
int i = 0;
while (parent != target) {
// Create C mutated strings + the current parent.
var candidates = Enumerable.Range(0, C + 1)
.Select(n => n > 0 ? Mutate(parent, P) : parent);
// select the fittest
parent = candidates.OrderByDescending(c => Fitness(target, c)).First();
++i;
Console.WriteLine(" #{0,6} {1,20} fitness: {2}",
i, parent, Fitness(target, parent));
}
Console.WriteLine("END: #{0,6} {1,20}", i, parent);
}
}
Example output:
START: PACQXJB CQPWEYKSVDCIOUPKUOJY fitness: 0
# 1 PALQXJB CQPWEYKSVDCIOUPEUOJY fitness: 1
# 2 PALQXJB CQPWEYKSVDEIOUPEUOJY fitness: 2
# 3 PALQXJB CQPWEYKSVDE OUPEUOJY fitness: 3
# 4 MALQOJB CQPWEYKSVDE OUPEUOJY fitness: 4
# 5 MALQOJB CQPWEYKSVKE OUPEUOJY fitness: 5
# 6 MALQOJB CQPWEYKLVKE OUPEUOES fitness: 7
# 7 MALQOJB CQPWEYKLVKE OUPEAOES fitness: 8
# 8 M LQOJB CQPWEYKLVKE OUPEAOES fitness: 8
# 9 M LQOJB CQPWEYKL KE OUPEAOES fitness: 8
# 10 M LHOJB CQPWEYKL KE OUPEAOES fitness: 9
# 11 M LHOJB CQPWEYKL KE OGYEAOEL fitness: 10
# 12 M LHOJB CQP EYKL KE OGYEAOEL fitness: 11
# 13 M THOJB CQP EYKL KE OGYEAOEL fitness: 12
# 14 M THOJB CQP ESKL KE OGYEAOEL fitness: 13
# 15 M THOJB CQP ESKL KE AGYEAOEL fitness: 14
# 16 M THHJBSCQP ESKL KE AGYEAOEL fitness: 15
# 17 M THHJBSCQP ES L KE AGYEAOEL fitness: 16
# 18 MXTHHJBSCQP ES L KE AGYEASEL fitness: 17
# 19 MXTHHJBSCOT ES L KE AGYEASEL fitness: 18
# 20 MXTHHJBSCOT ES L KE AGYEASEL fitness: 18
# 21 METHHJBSCOT GS L KE ACYEASEL fitness: 19
# 22 METHIJBSCOT GS L KE ACYEASEL fitness: 20
# 23 METHILBSCOT GS L KE ACYEASEL fitness: 20
# 24 METHILBSCOT GS L KE ACWEASEL fitness: 21
# 25 METHILBS OT GS LBKE ACWEASEL fitness: 22
# 26 METHILBS OT GS LBKE ACWEASEL fitness: 22
# 27 METHILBS OT IS LBKE ACWEASEL fitness: 23
# 28 METHILBS OT IS LBKE ACWEASEL fitness: 23
# 29 METHILBS OT IS LBKE ACWEASEL fitness: 23
# 30 METHILBS CT IS LPKE ACWEASEL fitness: 23
# 31 METHILBS CT IS LPKE ACWEASEL fitness: 23
# 32 METHILBS CT IS LPKE A WEASEL fitness: 24
# 33 METHILBS ET IS LPKE A WEASEL fitness: 24
# 34 METHILBS ET IS LPKE A WEASEL fitness: 24
# 35 METHILBS ET IS LPKE A WEASEL fitness: 24
# 36 METHILBS ET IS LPKE A WEASEL fitness: 24
# 37 METHILBS IT IS LPKE A WEASEL fitness: 25
# 38 METHILBS IT IS LPKE A WEASEL fitness: 25
# 39 METHILBS IT IS LPKE A WEASEL fitness: 25
# 40 METHILBS IT IS LPKE A WEASEL fitness: 25
# 41 METHILBS IT IS LPKE A WEASEL fitness: 25
# 42 METHILBS IT IS LPKE A WEASEL fitness: 25
# 43 METHINBS IT IS LPKE A WEASEL fitness: 26
# 44 METHINBS IT IS LPKE A WEASEL fitness: 26
# 45 METHINBS IT IS LPKE A WEASEL fitness: 26
# 46 METHINBS IT IS LIKE A WEASEL fitness: 27
# 47 METHINBS IT IS LIKE A WEASEL fitness: 27
# 48 METHINBS IT IS LIKE A WEASEL fitness: 27
# 49 METHINBS IT IS LIKE A WEASEL fitness: 27
# 50 METHINBS IT IS LIKE A WEASEL fitness: 27
# 51 METHINBS IT IS LIKE A WEASEL fitness: 27
# 52 METHINBS IT IS LIKE A WEASEL fitness: 27
# 53 METHINBS IT IS LIKE A WEASEL fitness: 27
# 54 METHINBS IT IS LIKE A WEASEL fitness: 27
# 55 METHINBS IT IS LIKE A WEASEL fitness: 27
# 56 METHINBS IT IS LIKE A WEASEL fitness: 27
# 57 METHINBS IT IS LIKE A WEASEL fitness: 27
# 58 METHINBS IT IS LIKE A WEASEL fitness: 27
# 59 METHINBS IT IS LIKE A WEASEL fitness: 27
# 60 METHINBS IT IS LIKE A WEASEL fitness: 27
# 61 METHINBS IT IS LIKE A WEASEL fitness: 27
# 62 METHINKS IT IS LIKE A WEASEL fitness: 28
END: # 62 METHINKS IT IS LIKE A WEASEL
C++
#include <string>
#include <cstdlib>
#include <iostream>
#include <cassert>
#include <algorithm>
#include <vector>
#include <ctime>
std::string allowed_chars = " ABCDEFGHIJKLMNOPQRSTUVWXYZ";
// class selection contains the fitness function, encapsulates the
// target string and allows access to it's length. The class is only
// there for access control, therefore everything is static. The
// string target isn't defined in the function because that way the
// length couldn't be accessed outside.
class selection
{
public:
// this function returns 0 for the destination string, and a
// negative fitness for a non-matching string. The fitness is
// calculated as the negated sum of the circular distances of the
// string letters with the destination letters.
static int fitness(std::string candidate)
{
assert(target.length() == candidate.length());
int fitness_so_far = 0;
for (int i = 0; i < target.length(); ++i)
{
int target_pos = allowed_chars.find(target[i]);
int candidate_pos = allowed_chars.find(candidate[i]);
int diff = std::abs(target_pos - candidate_pos);
fitness_so_far -= std::min(diff, int(allowed_chars.length()) - diff);
}
return fitness_so_far;
}
// get the target string length
static int target_length() { return target.length(); }
private:
static std::string target;
};
std::string selection::target = "METHINKS IT IS LIKE A WEASEL";
// helper function: cyclically move a character through allowed_chars
void move_char(char& c, int distance)
{
while (distance < 0)
distance += allowed_chars.length();
int char_pos = allowed_chars.find(c);
c = allowed_chars[(char_pos + distance) % allowed_chars.length()];
}
// mutate the string by moving the characters by a small random
// distance with the given probability
std::string mutate(std::string parent, double mutation_rate)
{
for (int i = 0; i < parent.length(); ++i)
if (std::rand()/(RAND_MAX + 1.0) < mutation_rate)
{
int distance = std::rand() % 3 + 1;
if(std::rand()%2 == 0)
move_char(parent[i], distance);
else
move_char(parent[i], -distance);
}
return parent;
}
// helper function: tell if the first argument is less fit than the
// second
bool less_fit(std::string const& s1, std::string const& s2)
{
return selection::fitness(s1) < selection::fitness(s2);
}
int main()
{
int const C = 100;
std::srand(time(0));
std::string parent;
for (int i = 0; i < selection::target_length(); ++i)
{
parent += allowed_chars[std::rand() % allowed_chars.length()];
}
int const initial_fitness = selection::fitness(parent);
for(int fitness = initial_fitness;
fitness < 0;
fitness = selection::fitness(parent))
{
std::cout << parent << ": " << fitness << "\n";
double const mutation_rate = 0.02 + (0.9*fitness)/initial_fitness;
std::vector<std::string> childs;
childs.reserve(C+1);
childs.push_back(parent);
for (int i = 0; i < C; ++i)
childs.push_back(mutate(parent, mutation_rate));
parent = *std::max_element(childs.begin(), childs.end(), less_fit);
}
std::cout << "final string: " << parent << "\n";
}
Example output:
BBQYCNLDIHG RWEXN PNGFTCMS: -203 ECPZEOLCHFJBCXTXFYLZQPDDQ KP: -177 HBSBGMKEEIM BUTUGWKWNRCGSZNN: -150 EEUCGNKDCHN RSSITKZPRBESYQK: -134 GBRFGNKDAINX TVRITIZPSBERXTH: -129 JEUFILLDDGNZCWYRIWFWSUAERZUI: -120 JESGILIGDJOZCWXRIWFVSXZESXXI: -109 JCSHILIIDIOZCTZOIUIVVXZEUVXI: -93 KDSHHLJIDIOZER LIUGXVXXFWW I: -76 KDSHGNMIDIOZHR LIUHXWXWFWW L: -69 LDSHHNMLDIOZKR LGSEXWXWFYV L: -59 LDSHHNMNDIOYKU LGSEXY WFYV M: -55 LCSHHNMLDHR IT LGSEZY WFYSBM: -44 LCSHHNMNBIR IT LGSEZY WFASBM: -36 LCSHHNMQBIQ JT LGQEZY WFASBM: -33 LCSIHNMRBIS JT LGQE Y WFASBM: -30 LESIHNMSBIS JR LGQE Y WFASBM: -27 LESIJNMSBIS JR LHOE A WFASBM: -21 LERIJNJSBIS JR LHOF A WFASEM: -19 LERIJNJSBIS JR LHLF A WFASEM: -16 NERIJNJS IS JR LHLF A WFASEM: -14 NERIJNJS IS JS LHLF A WFASEM: -13 NERIJNKS IS JS LHLF A WFASEM: -12 NERIJNKS IS JS LHKF A WFASEM: -11 NERIJNKS IS JS LHKF A WFASEM: -11 NERIJNKS IS JS LHKF A WEASEM: -10 NERIJNKS IS JS LHKF A WEASEM: -10 NERIJNKS IS JS LHKF A WEASEL: -9 NERIJNKS IS JS LHKF A WEASEL: -9 NETIJNKS IS JS LHKF A WEASEL: -7 NETIJNKS IS JS LHKF A WEASEL: -7 NETIJNKS IT JS LHKF A WEASEL: -6 NETIINKS IT JS LHKF A WEASEL: -5 NETIINKS IT JS LHKE A WEASEL: -4 NETHINKS IT JS LHKE A WEASEL: -3 NETHINKS IT JS LIKE A WEASEL: -2 NETHINKS IT JS LIKE A WEASEL: -2 NETHINKS IT JS LIKE A WEASEL: -2 NETHINKS IT JS LIKE A WEASEL: -2 NETHINKS IT JS LIKE A WEASEL: -2 NETHINKS IT JS LIKE A WEASEL: -2 METHINKS IT JS LIKE A WEASEL: -1 METHINKS IT JS LIKE A WEASEL: -1 METHINKS IT JS LIKE A WEASEL: -1 final string: METHINKS IT IS LIKE A WEASEL
Ceylon
import ceylon.random {
DefaultRandom
}
shared void run() {
value mutationRate = 0.05;
value childrenPerGeneration = 100;
value target = "METHINKS IT IS LIKE A WEASEL";
value alphabet = {' ', *('A'..'Z')};
value random = DefaultRandom();
value randomLetter => random.nextElement(alphabet);
function fitness(String a, String b) =>
count {for([c1, c2] in zipPairs(a, b)) c1 == c2};
function mutate(String string) =>
String {
for(letter in string)
if(random.nextFloat() < mutationRate)
then randomLetter
else letter
};
function makeCopies(String string) =>
{for(i in 1..childrenPerGeneration) mutate(string)};
function chooseFittest(String+ children) =>
children
.map((String element) => element->fitness(element, target))
.max(increasingItem)
.key;
variable value parent = String {for(i in 1..target.size) randomLetter};
variable value generationCount = 0;
function display() => print("``generationCount``: ``parent``");
display();
while(parent != target) {
parent = chooseFittest(parent, *makeCopies(parent));
generationCount++;
display();
}
print("mutated into target in ``generationCount`` generations!");
}
Clojure
Define the evolution parameters (values here per Wikipedia article), with a couple of problem constants.
(def c 100) ;number of children in each generation
(def p 0.05) ;mutation probability
(def target "METHINKS IT IS LIKE A WEASEL")
(def tsize (count target))
(def alphabet " ABCDEFGHIJLKLMNOPQRSTUVWXYZ")
Now the major functions. fitness simply counts the number of characters matching the target.
(defn fitness [s] (count (filter true? (map = s target))))
(defn perfectly-fit? [s] (= (fitness s) tsize))
(defn randc [] (rand-nth alphabet))
(defn mutate [s] (map #(if (< (rand) p) (randc) %) s))
Finally evolve. At each generation, print the generation number, the parent, and the parent's fitness.
(loop [generation 1, parent (repeatedly tsize randc)]
(println generation, (apply str parent), (fitness parent))
(if-not (perfectly-fit? parent)
(let [children (repeatedly c #(mutate parent))
fittest (apply max-key fitness parent children)]
(recur (inc generation), fittest))))
CLU
fitness = proc (s, t: string) returns (int)
f: int := 0
for i: int in int$from_to(1,string$size(s)) do
if s[i] ~= t[i] then f := f-1 end
end
return(f)
end fitness
mutate = proc (mut: int, s: string) returns (string)
own charset: string := " ABCDEFGHIJKLMNOPQRSTUVWXYZ"
out: array[char] := array[char]$predict(1,string$size(s))
for c: char in string$chars(s) do
if random$next(10000) < mut then
c := charset[1+random$next(string$size(charset))]
end
array[char]$addh(out,c)
end
return(string$ac2s(out))
end mutate
weasel = iter (mut, c: int, tgt: string) yields (string)
own charset: string := " ABCDEFGHIJKLMNOPQRSTUVWXYZ"
start: array[char] := array[char]$[]
for i: int in int$from_to(1,string$size(tgt)) do
array[char]$addh(start,charset[1+random$next(string$size(charset))])
end
cur: string := string$ac2s(start)
while true do
yield(cur)
if cur = tgt then break end
best: string := cur
best_fitness: int := fitness(cur, tgt)
for i: int in int$from_to(2,c) do
next: string := mutate(mut, cur)
next_fitness: int := fitness(next, tgt)
if best_fitness <= next_fitness then
best, best_fitness := next, next_fitness
end
end
cur := best
end
end weasel
start_up = proc ()
d: date := now()
random$seed(d.second + 60*(d.minute + 60*d.hour))
po: stream := stream$primary_output()
for m: string in weasel(100, 1000, "METHINKS IT IS LIKE A WEASEL") do
stream$putl(po, m)
end
end start_up- Output:
CQWBQKNNFKEOLFLXHGJYEWZYULBQ MQWBQKNNFKEOLFLXHGJYEWZYULBQ MQWBQKNNFKEOLFLXHGJYEWZEULBQ MEWBQKNNFKEOLFLXHGJYEWZEULBQ MEWBQKNNFKEOLFLXHKJYEWZEULBQ MEWBQKNNFKEOLFLXHKJYEWZEULBL MEWBQNNNFKEOLFLXHKJYEWZEULBL METBQNNNFKEOLFLXHKJYEWZEULBL METBQNNNFKEOLFLXHKJYE ZEULBL METBQNNSFKEOLFLXHKJYE ZEULBL METBQNNSFKEOLFLXHKEYE ZEULBL METBQNNSFKEOLFLXHKEYE ZEULEL METBQNNSFKEOLFLXIKEYE ZEULEL METEQNNSFKEOLFLLIKEYE ZEULEL METEQNNSFKEOLFLLIKE E ZEULEL METEQNKSFKEOLFLLIKE E ZEULEL METEQNKSFKEOLF LIKE E ZEULEL METEQNKSFKEOLF LIKE E ZEALEL METEQNKSFKEOIF LIKE E ZEALEL METEQNKSFKEOIF LIKE E ZEASEL METEQNKSFKTOIF LIKE E ZEASEL METEQNKS KTOIF LIKE E ZEASEL METEQNKS KTOIF LIKE E WEASEL METHQNKS KTOIF LIKE E WEASEL METHQNKS ITOIF LIKE E WEASEL METHQNKS ITOIF LIKE E WEASEL METHQNKS ITOIF LIKE A WEASEL METHQNKS IT IF LIKE A WEASEL METHQNKS IT IF LIKE A WEASEL METHQNKS IT IS LIKE A WEASEL METHQNKS IT IS LIKE A WEASEL METHQNKS IT IS LIKE A WEASEL METHINKS IT IS LIKE A WEASEL
COBOL
For testing purposes, you can comment out the first two sentences in the CONTROL-PARAGRAPH and the program will then use the same sequence of pseudo-random numbers on each run.
identification division.
program-id. evolutionary-program.
data division.
working-storage section.
01 evolving-strings.
05 target pic a(28)
value 'METHINKS IT IS LIKE A WEASEL'.
05 parent pic a(28).
05 offspring-table.
10 offspring pic a(28)
occurs 50 times.
01 fitness-calculations.
05 fitness pic 99.
05 highest-fitness pic 99.
05 fittest pic 99.
01 parameters.
05 character-set pic a(27)
value 'ABCDEFGHIJKLMNOPQRSTUVWXYZ '.
05 size-of-generation pic 99
value 50.
05 mutation-rate pic 99
value 5.
01 counters-and-working-variables.
05 character-position pic 99.
05 randomization.
10 random-seed pic 9(8).
10 random-number pic 99.
10 random-letter pic 99.
05 generation pic 999.
05 child pic 99.
05 temporary-string pic a(28).
procedure division.
control-paragraph.
accept random-seed from time.
move function random(random-seed) to random-number.
perform random-letter-paragraph,
varying character-position from 1 by 1
until character-position is greater than 28.
move temporary-string to parent.
move zero to generation.
perform output-paragraph.
perform evolution-paragraph,
varying generation from 1 by 1
until parent is equal to target.
stop run.
evolution-paragraph.
perform mutation-paragraph varying child from 1 by 1
until child is greater than size-of-generation.
move zero to highest-fitness.
move 1 to fittest.
perform check-fitness-paragraph varying child from 1 by 1
until child is greater than size-of-generation.
move offspring(fittest) to parent.
perform output-paragraph.
output-paragraph.
display generation ': ' parent.
random-letter-paragraph.
move function random to random-number.
divide random-number by 3.80769 giving random-letter.
add 1 to random-letter.
move character-set(random-letter:1)
to temporary-string(character-position:1).
mutation-paragraph.
move parent to temporary-string.
perform character-mutation-paragraph,
varying character-position from 1 by 1
until character-position is greater than 28.
move temporary-string to offspring(child).
character-mutation-paragraph.
move function random to random-number.
if random-number is less than mutation-rate
then perform random-letter-paragraph.
check-fitness-paragraph.
move offspring(child) to temporary-string.
perform fitness-paragraph.
fitness-paragraph.
move zero to fitness.
perform character-fitness-paragraph,
varying character-position from 1 by 1
until character-position is greater than 28.
if fitness is greater than highest-fitness
then perform fittest-paragraph.
character-fitness-paragraph.
if temporary-string(character-position:1) is equal to
target(character-position:1) then add 1 to fitness.
fittest-paragraph.
move fitness to highest-fitness.
move child to fittest.
- Output:
000: YZPLJKKFEZTWMSGAPVMUZBKBLLRS 001: YZPLJKKFEZTWMSGAPVMUZBKBLLRS 002: YZPLJKKFEZTWMS APVMUZBKBLLRS 003: JZPLJKKFEZTWMS AIVMUZBKBLLRS 004: JZPLJKKFEZTWMS AIVBUABKBLLRS 005: JZPLJKKFEZTWIS AIVBUABKBLLRS 006: JZPLJKKFEZTWIS AIVBUABKBLLRS 007: MVPLXKKFECTWIS AIVBUABKBLLRS 008: MVPLXKKSECTWIS AIVBUABKBLLRS 009: MVPLCKKSUCTWIS AIVBUABKBLLRS 010: MVPLCKKSUCTJIS LIVBVABKBLLRS 011: MVPLCKKSUCTJIS LIVBVABKBLSRS 012: MVPLCKKSUCTJIS LIVBQABKBLSRS 013: MVPLCKKSUCTJIS LIVBQABKBLSRS 014: MEPLCKKSUCTJIS LIVBQABKBLSRS 015: MEPVCKKSUCTJIS LIVBFABKBLSRS 016: MEPVCKKSUCTJIS LIVBFABKBLSRE 017: MEPVCKKSUCTJIS LIVBFABKBLSEE 018: MEPVCKKSUCTJIS LIVBFABWBLSEE 019: MEPVCKKSUCTJIS LIVBFABWBLSEE 020: MEPXCKKSUCTJIS LIVBFABWBLSEE 021: MEPXCKKSUCTJIS LIVBFABWBLSEE 022: MEPXCKKSUSTJIS LIVBFABWBLSEE 023: MEPXCKKSUSTJIS LIVBFABWBASEE 024: MEPXCKKSUSTJIS LIVEFABWBASEM 025: MEPXCKKSUSTJIS LIVEFABWEASEM 026: MEPXCKKSUSTJIS LIVEFABWEASEM 027: MEPXCKKSUITJIS LIVEFABWEASEM 028: MEPXCNKSUITJIS LIVEFABWEASEM 029: MEPXCNKSUITJIS LIVEFABWEASEM 030: MEPXCNKS ITJIS LIVEFABWEASEM 031: MEPXCNKS ITJIS LIVEFABWEASEM 032: MEPXCNKS ITJIS LIVEFABWEASEM 033: MEPXCNKS ITJIS LIVEFABWEASEM 034: MEPXCNKS ITNIS LIVEFABWEASEM 035: METICNKS ITNIS LIVEYABWEASEM 036: METICNKS ITNIS LIVEYABWEASEM 037: METICNKS ITMIS LIVEYABWEASEM 038: METIHNKS ITMIS LIVEYABWEASEM 039: METIHNKS ITMIS LIVEYABWEASEM 040: METIHNKS ITMIS LIKEYABWEASEM 041: METIHNKS IT IS LIKEYABWEASEM 042: METIHNKS IT IS LIKEYABWEASEM 043: METIHNKS IT IS LIKEPABWEASEM 044: METIHNKS IT IS LIKEPABWEASEM 045: METHHNKS IT IS LIKEPABWEASEM 046: METHHNKS IT IS LIKEPABWEASEM 047: METHHNKS IT IS LIKEPABWEASEM 048: METHHNKS IT IS LIKEPABWEASEM 049: METHHNKS IT IS LIKEPABWEASEM 050: METHHNKS IT IS LIKEPABWEASEM 051: METHHNKS IT IS LIKEPABWEASEM 052: METHHNKS IT IS LIKEPABWEASEL 053: METHHNKS IT IS LIKEPABWEASEL 054: METHHNKS IT IS LIKEPA WEASEL 055: METHHNKS IT IS LIKEPA WEASEL 056: METHHNKS IT IS LIKEPA WEASEL 057: METHINKS IT IS LIKEPA WEASEL 058: METHINKS IT IS 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088: METHINKS IT IS LIKETA WEASEL 089: METHINKS IT IS LIKETA WEASEL 090: METHINKS IT IS LIKETA WEASEL 091: METHINKS IT IS LIKETA WEASEL 092: METHINKS IT IS LIKETA WEASEL 093: METHINKS IT IS LIKETA WEASEL 094: METHINKS IT IS LIKETA WEASEL 095: METHINKS IT IS LIKETA WEASEL 096: METHINKS IT IS LIKETA WEASEL 097: METHINKS IT IS LIKETA WEASEL 098: METHINKS IT IS LIKETA WEASEL 099: METHINKS IT IS LIKETA WEASEL 100: METHINKS IT IS LIKETA WEASEL 101: METHINKS IT IS LIKETA WEASEL 102: METHINKS IT IS LIKETA WEASEL 103: METHINKS IT IS LIKETA WEASEL 104: METHINKS IT IS LIKETA WEASEL 105: METHINKS IT IS LIKETA WEASEL 106: METHINKS IT IS LIKETA WEASEL 107: METHINKS IT IS LIKETA WEASEL 108: METHINKS IT IS LIKETA WEASEL 109: METHINKS IT IS LIKETA WEASEL 110: METHINKS IT IS LIKETA WEASEL 111: METHINKS IT IS LIKETA WEASEL 112: METHINKS IT IS LIKETA WEASEL 113: METHINKS IT IS LIKETA WEASEL 114: METHINKS IT IS LIKETA WEASEL 115: METHINKS IT IS LIKETA WEASEL 116: METHINKS IT IS LIKETA WEASEL 117: METHINKS IT IS LIKETA WEASEL 118: METHINKS IT IS LIKETA WEASEL 119: METHINKS IT IS LIKETA WEASEL 120: METHINKS IT IS LIKETA WEASEL 121: METHINKS IT IS LIKETA WEASEL 122: METHINKS IT IS LIKETA WEASEL 123: METHINKS IT IS LIKETA WEASEL 124: METHINKS IT IS LIKETA WEASEL 125: METHINKS IT IS LIKETA WEASEL 126: METHINKS IT IS LIKETA WEASEL 127: METHINKS IT IS LIKEDA WEASEL 128: METHINKS IT IS LIKEDA WEASEL 129: METHINKS IT IS LIKEDA WEASEL 130: METHINKS IT IS LIKEKA WEASEL 131: METHINKS IT IS LIKEKA WEASEL 132: METHINKS IT IS LIKEKA WEASEL 133: METHINKS IT IS LIKEKA WEASEL 134: METHINKS IT IS LIKEKA WEASEL 135: METHINKS IT IS LIKEKA WEASEL 136: METHINKS IT IS LIKEKA WEASEL 137: METHINKS IT IS LIKEKA WEASEL 138: METHINKS IT IS LIKEKA WEASEL 139: METHINKS IT IS LIKEKA WEASEL 140: METHINKS IT IS LIKEKA WEASEL 141: METHINKS IT IS LIKEKA WEASEL 142: METHINKS IT IS LIKEKA WEASEL 143: METHINKS IT IS LIKEKA WEASEL 144: METHINKS IT IS LIKEKA WEASEL 145: METHINKS IT IS LIKEKA WEASEL 146: METHINKS IT IS LIKEKA WEASEL 147: METHINKS IT IS LIKEKA WEASEL 148: METHINKS IT IS LIKEKA WEASEL 149: METHINKS IT IS LIKEKA WEASEL 150: METHINKS IT IS LIKEKA WEASEL 151: METHINKS IT IS LIKEKA WEASEL 152: METHINKS IT IS LIKEKA WEASEL 153: METHINKS IT IS LIKEKA WEASEL 154: METHINKS IT IS LIKEKA WEASEL 155: METHINKS IT IS LIKEKA WEASEL 156: METHINKS IT IS LIKEKA WEASEL 157: METHINKS IT IS LIKEKA WEASEL 158: METHINKS IT IS LIKEKA WEASEL 159: METHINKS IT IS LIKEKA WEASEL 160: METHINKS IT IS LIKEKA WEASEL 161: METHINKS IT IS LIKEKA WEASEL 162: METHINKS IT IS LIKEKA WEASEL 163: METHINKS IT IS LIKEKA WEASEL 164: METHINKS IT IS LIKEHA WEASEL 165: METHINKS IT IS LIKEHA WEASEL 166: METHINKS IT IS LIKEHA WEASEL 167: METHINKS IT IS LIKEHA WEASEL 168: METHINKS IT IS LIKEHA WEASEL 169: METHINKS IT IS LIKEHA WEASEL 170: METHINKS IT IS LIKEYA WEASEL 171: METHINKS IT IS LIKEYA WEASEL 172: METHINKS IT IS LIKEYA WEASEL 173: METHINKS IT IS LIKEYA WEASEL 174: METHINKS IT IS LIKEYA WEASEL 175: METHINKS IT IS LIKEYA WEASEL 176: METHINKS IT IS LIKEYA WEASEL 177: METHINKS IT IS LIKEYA WEASEL 178: METHINKS IT IS LIKEYA WEASEL 179: METHINKS IT IS LIKEYA WEASEL 180: METHINKS IT IS LIKEYA WEASEL 181: METHINKS IT IS LIKEYA WEASEL 182: METHINKS IT IS LIKEYA WEASEL 183: METHINKS IT IS LIKEYA WEASEL 184: METHINKS IT IS LIKEYA WEASEL 185: METHINKS IT IS LIKEYA WEASEL 186: METHINKS IT IS LIKEYA WEASEL 187: METHINKS IT IS LIKEYA WEASEL 188: METHINKS IT IS LIKEYA WEASEL 189: METHINKS IT IS LIKEZA WEASEL 190: METHINKS IT IS LIKEZA WEASEL 191: METHINKS IT IS LIKEZA WEASEL 192: METHINKS IT IS LIKEZA WEASEL 193: METHINKS IT IS LIKEZA WEASEL 194: METHINKS IT IS LIKE A WEASEL
ColdFusion
<Cfset theString = 'METHINKS IT IS LIKE A WEASEL'>
<cfparam name="parent" default="">
<Cfset theAlphabet = "ABCDEFGHIJKLMNOPQRSTUVWXYZ ">
<Cfset fitness = 0>
<Cfset children = 3>
<Cfset counter = 0>
<Cfloop from="1" to="#children#" index="child">
<Cfparam name="child#child#" default="">
<Cfparam name="fitness#child#" default=0>
</Cfloop>
<Cfloop condition="fitness lt 1">
<Cfset oldparent = parent>
<Cfset counter = counter + 1>
<cfloop from="1" to="#children#" index="child">
<Cfset thischild = ''>
<Cfloop from="1" to="#len(theString)#" index="i">
<cfset Mutate = Mid(theAlphabet, RandRange(1, 28), 1)>
<cfif fitness eq 0>
<Cfset thischild = thischild & mutate>
<Cfelse>
<Cfif Mid(theString, i, 1) eq Mid(variables["child" & child], i, 1)>
<Cfset thischild = thischild & Mid(variables["child" & child], i, 1)>
<Cfelse>
<cfset MutateChance = 1/fitness>
<Cfset MutateChanceRand = rand()>
<Cfif MutateChanceRand lte MutateChance>
<Cfset thischild = thischild & mutate>
<Cfelse>
<Cfset thischild = thischild & Mid(variables["child" & child], i, 1)>
</Cfif>
</Cfif>
</cfif>
</Cfloop>
<Cfset variables["child" & child] = thischild>
</cfloop>
<cfloop from="1" to="#children#" index="child">
<Cfset thisChildFitness = 0>
<Cfloop from="1" to="#len(theString)#" index="i">
<Cfif Mid(variables["child" & child], i, 1) eq Mid(theString, i, 1)>
<Cfset thisChildFitness = thisChildFitness + 1>
</Cfif>
</Cfloop>
<Cfset variables["fitness" & child] = (thisChildFitness)/len(theString)>
<Cfif variables["fitness" & child] gt fitness>
<Cfset fitness = variables["fitness" & child]>
<Cfset parent = variables["child" & child]>
</Cfif>
</cfloop>
<Cfif parent neq oldparent>
<Cfoutput>###counter# #numberformat(fitness*100, 99)#% fit: #parent#<br></Cfoutput><cfflush>
</Cfif>
</Cfloop>
#1 7% fit: VOPJOBSYPTTUNYYSAFHTPJUIAIL #2 18% fit: FQUFHEKPLXTQISYZZRIEVQWBHRC #3 21% fit: MGTUKIRICATKDDMSIUNDERUAASKT #33 29% fit: M THILKORWP XSRVOLV GVIRVJHE #34 36% fit: MEBHRNTSYPH IHTCHMH LGWBAFZ #37 39% fit: MSTHIWKLIHU KSSLECR Z WGUMZE #61 43% fit: METHINKA RT ZRQCEFVEAMWKZEBA #62 50% fit: METHINKA GT RLQAOHVSAXWNAS A #67 54% fit: MESHINKT IGBWSRLIEEAF WERYWH #72 57% fit: METHINKE VT YBUJNRXRA W XSEL #129 64% fit: METHINKS ITCIEHLPNB A YYAAPL #156 68% fit: METHINKS IT IHIWJKY I W GSAL #177 71% fit: METHINKS IT IS RIPRPA BEAVYN #180 75% fit: METHINKS IT IS OI BAA TEABBL #185 79% fit: METHINKS IT IS LIQEWA EEARLX #197 82% fit: METHINKS IT IS LIKP OKWEASMU #222 86% fit: METHINKS IT IS LIKESG WEALEH #245 89% fit: METHINKS IT IS LIKEOA GEAQEL #304 93% fit: METHINKS IT IS LIKE A WESSYL #349 96% fit: METHINKS IT IS LIKE A WEASOL #360 100% fit: METHINKS IT IS LIKE A WEASEL
Commodore BASIC
Finding the target string takes roughly two hours on a Commodore 64.
10 N=100:P=0.05:TI$="000000"
20 Z$="METHINKS IT IS LIKE A WEASEL"
30 L=LEN(Z$)
40 DIMX(N,L)
50 FORI=1TOL
60 IFMID$(Z$,I,1)=" "THENX(0,I)=0:GOTO80
70 X(0,I)=ASC(MID$(Z$,I))-64
80 NEXT
90 FORK=1TON:FORI=1TOL:X(K,I)=INT(RND(0)*27):NEXT:NEXT
100 S=-100:B=0
110 K=B:GOSUB300
120 FORK=1TON:IFK=BTHEN150
130 FORI=1TOL:IFRND(.)<PTHENX(K,I)=INT(RND(0)*27)
140 NEXT
150 NEXT
160 S=-100:B=0
170 FORK=1TON
180 F=0:FORI=1TOL:IFX(K,I)<>X(0,I)THENF=F-1:IFF<STHENI=L
190 NEXT:IFF>STHENS=F:B=K
200 NEXT
210 PRINT"BEST:"B;"SCORE:"S
220 IFS=0THEN270
230 FORK=1TON:IFK=BTHEN250
240 FORI=1TOL:X(K,I)=X(B,I):NEXT
250 NEXT
260 GOTO110
270 PRINT"WE HAVE A WEASEL!":K=B:GOSUB300
280 PRINT"TIME:"TI$:END
300 FORI=1TOL:IFX(K,I)THENPRINTCHR$(64+X(K,I));:GOTO320
310 PRINT" ";
320 NEXT:PRINT"<":RETURN
Output during calculation (excerpt):
BEST: 23 SCORE:-21 PJKJELKRRIWZITLOCKXTNYLNASEY< BEST: 3 SCORE:-20 PJKJENKRRIWZITLOCKXTNJLNASEY< BEST: 14 SCORE:-19 PJKJENKSRIWZITLOCKXTNJLNASEY< BEST: 79 SCORE:-18 PJKJENKSRIW ITLOCKXTNJLNASEY< BEST: 50 SCORE:-17 PJKUENKSRIW ITLOCKXTN LNASEY< BEST: 12 SCORE:-16 PJKUINKSRIW ITLOCKHTN LNASEY< BEST: 8 SCORE:-15 PJKUINKSSIK ITLOCKHTA LNASEY< BEST: 21 SCORE:-14 PJKHINKSSIK ITLOCKHTA LNASEY< BEST: 30 SCORE:-13 PJTHINKSSIK ITTOCKHTA LOASEY< BEST: 48 SCORE:-12 PJTHINKS IK ITTOCKHTA LOASEY< BEST: 1 SCORE:-12 PJTHINKS IK ITTOCKHTA LOASEY< BEST: 29 SCORE:-11 PJTHINKS IK IT OCKHTA LOASEY<
Common Lisp
(defun fitness (string target)
"Closeness of string to target; lower number is better"
(loop for c1 across string
for c2 across target
count (char/= c1 c2)))
(defun mutate (string chars p)
"Mutate each character of string with probablity p using characters from chars"
(dotimes (n (length string))
(when (< (random 1.0) p)
(setf (aref string n) (aref chars (random (length chars))))))
string)
(defun random-string (chars length)
"Generate a new random string consisting of letters from char and specified length"
(do ((n 0 (1+ n))
(str (make-string length)))
((= n length) str)
(setf (aref str n) (aref chars (random (length chars))))))
(defun evolve-string (target string chars c p)
"Generate new mutant strings, and choose the most fit string"
(let ((mutated-strs (list string)))
(dotimes (n c)
(push (mutate (copy-seq string) chars p) mutated-strs))
(reduce #'(lambda (s0 s1)
(if (< (fitness s0 target)
(fitness s1 target))
s0
s1))
mutated-strs)))
(defun evolve-gens (target c p)
(let ((chars " ABCDEFGHIJKLMNOPQRSTUVWXYZ"))
(do ((parent (random-string chars (length target))
(evolve-string target parent chars c p))
(n 0 (1+ n)))
((string= target parent) (format t "Generation ~A: ~S~%" n parent))
(format t "Generation ~A: ~S~%" n parent))))
Sample output:
CL-USER> (evolve-gens "METHINKS IT IS LIKE A WEASEL" 100 0.05) Generation 0: "IFNGR ACQNOAWQZYHNIUPLRHTPCP" Generation 1: "IUNGRHAC NOAWQZYHNIUPLRHTPCP" Generation 2: "IUNGRHAC YO WQZYHNIUPLRHTPCP" Generation 3: "IUNGRHKC YO WQZYHNIUPLJHTPRP" Generation 4: "IUNGRHKC IO WQZYHVIUPLVHTPRP" Generation 5: "IUNGRNKC IO WQZYHVIUPLVHNPRP" Generation 6: "IUNGRNKC IO WQZYHVIUPLVHNPRP" Generation 7: "IENGRNKC IO WQZYHVIUPLVHNPRP" Generation 8: "IENGRNKC IO WQZYHVEURLVHNPRP" Generation 9: "IENMRNKC IO WQZYHVE RLVHNPRP" Generation 10: "IENMRNKC IO WQZYHVE RLVHNPRP" Generation 11: "IENMRNKC IO WQZYHVE RLVHNPRP" Generation 12: "IEZMRNKC IO WQZYAVE RLVHNSRP" Generation 13: "IEZMRNKC IO WQZYIVE RLVHNSRP" Generation 14: "IEZMRNKC IO WQZYIKE RLVHNSRP" Generation 15: "IEZMRNKC IO WQZYIKE RLVHNSRL" Generation 16: "IEZ INKC IZ WQZYIKE RLVHNSRL" Generation 17: "IET INKC IZ WQZYIKE RLVHNSRL" Generation 18: "IET INKC IZ WQZYIKE RLVHNSEL" Generation 19: "IET INKC IZ WQZ IKE RLVHASEL" Generation 20: "GET INKC IZ WSZ IKE RLVHASEL" Generation 21: "GET INKC IZ WSZ IKE RLVHASEL" Generation 22: "GET INKC IZ WSZ IKE RLVHASEL" Generation 23: "GET INKC IZ ISZ IKE RLVHASEL" Generation 24: "GET INKC IZ ISZ IKE RLWHASEL" Generation 25: "MET INKC IZ ISZ IKE OLWHASEL" Generation 26: "MET INKC IZ ISZ IKE OLWHASEL" Generation 27: "MET INKC IZ ISZ IKE ALWHASEL" Generation 28: "MET INKC IZ ISZ IKE A WHASEL" Generation 29: "METHINKC IZ ISZ IKE A WHASEL" Generation 30: "METHINKC IZ ISZ IKE A WHASEL" Generation 31: "METHINKC IZ ISZ IKE A WHASEL" Generation 32: "METHINKC IZ ISZ IKE A WEASEL" Generation 33: "METHINKC IZ ISZ IKE A WEASEL" Generation 34: "METHINKC IZ ISZ IKE A WEASEL" Generation 35: "METHINKC IT ISZLIKD A WEASEL" Generation 36: "METHINKC IT ISZLIKD A WEASEL" Generation 37: "METHINKC IT ISZLIKD A WEASEL" Generation 38: "METHINKC IT ISZLIKD A WEASEL" Generation 39: "METHINKC IT ISZLIKD A WEASEL" Generation 40: "METHINKC IT ISZLIKE A WEASEL" Generation 41: "METHINKC IT IS LIKE A WEASEL" Generation 42: "METHINKC IT IS LIKE A WEASEL" Generation 43: "METHINKS IT IS LIKE A WEASEL"
Mutates one character at a time, with only on offspring each generation (which competes against the parent):
(defun unfit (s1 s2)
(loop for a across s1
for b across s2 count(char/= a b)))
(defun mutate (str alp n) ; n: number of chars to mutate
(let ((out (copy-seq str)))
(dotimes (i n) (setf (char out (random (length str)))
(char alp (random (length alp)))))
out))
(defun evolve (changes alpha target)
(loop for gen from 1
with f2 with s2
with str = (mutate target alpha 100)
with fit = (unfit target str)
while (plusp fit) do
(setf s2 (mutate str alpha changes)
f2 (unfit target s2))
(when (> fit f2)
(setf str s2 fit f2)
(format t "~5d: ~a (~d)~%" gen str fit))))
(evolve 1 " ABCDEFGHIJKLMNOPQRSTUVWXYZ" "METHINKS IT IS LIKE A WEASEL")
outupt
44: DYZTOREXDML ZCEUCSHRVHBEPGJE (26)
57: DYZTOREXDIL ZCEUCSHRVHBEPGJE (25)
83: DYZTOREX IL ZCEUCSHRVHBEPGJE (24)
95: MYZTOREX IL ZCEUCSHRVHBEPGJE (23)
186: MYZTOREX IL ZCEUISHRVHBEPGJE (22)
208: MYZTOREX IL ZCEUISH VHBEPGJE (21)
228: MYZTOREX IL ZCEUISH VHBEPGEE (20)
329: MYZTOREX IL ZCEUIKH VHBEPGEE (19)
330: MYTTOREX IL ZCEUIKH VHBEPGEE (18)
354: MYTHOREX IL ZCEUIKH VHBEPGEE (17)
365: MYTHOREX IL ICEUIKH VHBEPGEE (16)
380: MYTHOREX IL ISEUIKH VHBEPGEE (15)
393: METHOREX IL ISEUIKH VHBEPGEE (14)
407: METHORKX IL ISEUIKH VHBEPGEE (13)
443: METHORKX IL ISEUIKH VHBEPSEE (12)
455: METHORKX IL ISEUIKE VHBEPSEE (11)
477: METHIRKX IL ISEUIKE VHBEPSEE (10)
526: METHIRKS IL ISEUIKE VHBEPSEE (9)
673: METHIRKS IL ISEUIKE VHBEPSEL (8)
800: METHINKS IL ISEUIKE VHBEPSEL (7)
875: METHINKS IL ISEUIKE AHBEPSEL (6)
941: METHINKS IL ISEUIKE AHBEASEL (5)
1175: METHINKS IT ISEUIKE AHBEASEL (4)
1214: METHINKS IT ISELIKE AHBEASEL (3)
1220: METHINKS IT IS LIKE AHBEASEL (2)
1358: METHINKS IT IS LIKE AHWEASEL (1)
2610: METHINKS IT IS LIKE A WEASEL (0)
D
import std.stdio, std.random, std.algorithm, std.range, std.ascii;
enum target = "METHINKS IT IS LIKE A WEASEL"d;
enum C = 100; // Number of children in each generation.
enum P = 0.05; // Mutation probability.
enum fitness = (dchar[] s) => target.zip(s).count!q{ a[0] != a[1] };
dchar rnd() { return (uppercase ~ " ")[uniform(0, $)]; }
enum mut = (dchar[] s) => s.map!(a => uniform01 < P ? rnd : a).array;
void main() {
auto parent = generate!rnd.take(target.length).array;
for (auto gen = 1; parent != target; gen++) {
// parent = parent.repeat(C).map!mut.array.max!fitness;
parent = parent.repeat(C).map!mut.array
.minPos!((a, b) => a.fitness < b.fitness)[0];
writefln("Gen %2d, dist=%2d: %s", gen, parent.fitness, parent);
}
}
- Output:
Generation 0, dist=25: PTJNKPFVJFTDRSDVNUB ESJGU MF Generation 1, dist=18: PEKNKNKSBFTDISDVIUB ESJEP MF Generation 2, dist=12: NETVKNKS FTDISDLIUE EIJEPSEF Generation 3, dist= 8: NETVONKS ITDISDLIUE AIWEASEF Generation 4, dist= 8: NETVONKS ITDISDLIUE AIWEASEF Generation 5, dist= 6: NETHONKS ITDIS LINE AIWEASEW Generation 6, dist= 5: NETHINKS ITSIS LINE AIWEASEW Generation 7, dist= 5: NETHINKS ITSIS LINE AIWEASEW Generation 8, dist= 4: NETHINKS ITSIS LINE A WEASEW Generation 9, dist= 3: METHINKS ITSIS LINE A WEASEW Generation 10, dist= 3: METHINKS ITSIS LINE A WEASEW Generation 11, dist= 3: METHINKS ITSIS LINE A WEASEW Generation 12, dist= 2: METHINKS IT IS LINE A WEASEW Generation 13, dist= 2: METHINKS IT IS LINE A WEASEW Generation 14, dist= 1: METHINKS IT IS LIKE A WEASEW Generation 15, dist= 1: METHINKS IT IS LIKE A WEASEW Generation 16, dist= 1: METHINKS IT IS LIKE A WEASEW Generation 17, dist= 1: METHINKS IT IS LIKE A WEASEW Generation 18, dist= 1: METHINKS IT IS LIKE A WEASEW Generation 19, dist= 1: METHINKS IT IS LIKE A WEASEW Generation 20, dist= 1: METHINKS IT IS LIKE A WEASEW Generation 21, dist= 1: METHINKS IT IS LIKE A WEASEW Generation 22, dist= 1: METHINKS IT IS LIKE A WEASEW Generation 23, dist= 1: METHINKS IT IS LIKE A WEASEW Generation 24, dist= 0: METHINKS IT IS LIKE A WEASEL
Dart
import 'dart:math';
class EvoAlgo {
static final String target = "METHINKS IT IS LIKE A WEASEL";
static final List<String> possibilities = "ABCDEFGHIJKLMNOPQRSTUVWXYZ ".split('');
static int c = 100; // Number of spawn per generation
static double minMutateRate = 0.09;
static int perfectFitness = target.length;
static String parent = '';
static Random rand = Random();
static int fitness(String trial) {
int retVal = 0;
for (int i = 0; i < trial.length; i++) {
if (trial[i] == target[i]) retVal++;
}
return retVal;
}
static double newMutateRate() {
return (((perfectFitness - fitness(parent)) / perfectFitness) * (1 - minMutateRate));
}
static String mutate(String parent, double rate) {
String retVal = '';
for (int i = 0; i < parent.length; i++) {
retVal += (rand.nextDouble() <= rate)
? possibilities[rand.nextInt(possibilities.length)]
: parent[i];
}
return retVal;
}
static void main() {
parent = mutate(target, 1);
int iter = 0;
while (parent != target) {
double rate = newMutateRate();
iter++;
if (iter % 100 == 0) {
print('$iter: $parent, fitness: ${fitness(parent)}, rate: $rate');
}
String bestSpawn;
int bestFit = 0;
for (int i = 0; i < c; i++) {
String spawn = mutate(parent, rate);
int fit = fitness(spawn);
if (fit > bestFit) {
bestSpawn = spawn;
bestFit = fit;
}
}
if (bestFit > fitness(parent)) {
parent = bestSpawn;
}
}
print('$parent, $iter');
}
}
void main() {
EvoAlgo.main();
}
- Output:
100: MFTHINFU IR N WLEKWKA WQZKYM, fitness: 13, rate: 0.4875 200: MXTHINPJ HR NNTLEKF A WDZHEH, fitness: 14, rate: 0.455 300: YNTHINPJ IK IS LEKNNA ADZHEL, fitness: 16, rate: 0.39 400: XETHI PS IKXIS LBKNDA QEUSEL, fitness: 18, rate: 0.325 500: NETHINPS I IS IBKLNA WEASEL, fitness: 21, rate: 0.2275 METHINKS IT IS LIKE A WEASEL, 551
Delphi
See Pascal.
E
pragma.syntax("0.9")
pragma.enable("accumulator")
def target := "METHINKS IT IS LIKE A WEASEL"
def alphabet := "ABCDEFGHIJKLMNOPQRSTUVWXYZ "
def C := 100
def RATE := 0.05
def randomCharString() {
return E.toString(alphabet[entropy.nextInt(alphabet.size())])
}
def fitness(string) {
return accum 0 for i => ch in string {
_ + (ch == target[i]).pick(1, 0)
}
}
def mutate(string, rate) {
return accum "" for i => ch in string {
_ + (entropy.nextDouble() < rate).pick(randomCharString(), E.toString(ch))
}
}
def weasel() {
var parent := accum "" for _ in 1..(target.size()) { _ + randomCharString() }
var generation := 0
while (parent != target) {
println(`$generation $parent`)
def copies := accum [] for _ in 1..C { _.with(mutate(parent, RATE)) }
var best := parent
for c in copies {
if (fitness(c) > fitness(best)) {
best := c
}
}
parent := best
generation += 1
}
println(`$generation $parent`)
}
weasel()EasyLang
target$ = "METHINKS IT IS LIKE A WEASEL"
abc$[] = strchars " ABCDEFGHIJLKLMNOPQRSTUVWXYZ"
P = 0.05
C = 100
func fitness trial$ .
for i to len trial$
res += if substr trial$ i 1 <> substr target$ i 1
.
return res
.
func$ mutate parent$ .
for c$ in strchars parent$
if randomf < P
res$ &= abc$[random len abc$[]]
else
res$ &= c$
.
.
return res$
.
for i to len target$
parent$ &= abc$[random len abc$[]]
.
while fitness parent$ > 0
copies$[] = [ ]
for i to C
copies$[] &= mutate parent$
.
parent$ = copies$[1]
for s$ in copies$[]
if fitness s$ < fitness parent$
parent$ = s$
.
.
step += 1
print step & " " & parent$
.
EchoLisp
(require 'sequences)
(define ALPHABET (list->vector ["A" .. "Z"] ))
(vector-push ALPHABET " ")
(define (fitness source target) ;; score >=0, best is 0
(for/sum [(s source)(t target)]
(if (= s t) 0 1)))
(define (mutate source rate)
(for/string [(s source)]
(if (< (random) rate) [ALPHABET (random 27)] s)))
(define (select parent target rate copies (copy) (score))
(define best (fitness parent target))
(define selected parent)
(for [(i copies)]
(set! copy (mutate parent rate))
(set! score (fitness copy target))
(when (< score best)
(set! selected copy)
(set! best score)))
selected )
(define MUTATION_RATE 0.05) ;; 5% chances to change
(define COPIES 100)
(define TARGET "METHINKS IT IS LIKE A WEASEL")
(define (task (rate MUTATION_RATE) (copies COPIES) (target TARGET) (score))
(define parent ;; random source
(for/string
[(i (string-length target))] [ALPHABET (random 27)]))
(for [(i (in-naturals))]
(set! score (fitness parent target))
(writeln i parent 'score score)
#:break (zero? score)
(set! parent (select parent target rate copies))
))
- Output:
(task) 0 "TNCEKMNVYOW NSMSZ BZDODMMAXE" score 26 1 "TNCEKBNVYOW NSMSZ AZDODMMAEE" score 25 2 "TNCEKINVYOW NSMSZKEZDODMMAEE" score 23 3 "TNCEKIKVYOW NSMSZKEZDODMMAEE" score 22 4 "TNCEKIKVYOW NSMSZKEZDOWMMAEE" score 21 5 "TNCEKIKVYOW NSMSZKEZDOWMMAEE" score 21 6 "MNCEKIKVYOW NSMSZKEZSOWMMAEE" score 20 7 "MNCEKIKAYOE NSMLZKEZSOWMMAEE" score 19 8 "MNCEKIKAYOE NSMLZKEZS WMMAEE" score 18 9 "MNCEKIKAYOE ISMLZKEZS WMMAEE" score 17 10 "MECEKIKAYBE ISMLZKEZS WMMAEE" score 16 11 "MECEKLKAYBE ISMLZKE S WMMAEE" score 15 12 "METEKZKAYBE ISMLZKE S WMMAEE" score 14 13 "METEKZKAYBE ISMLZKE S WMMSEE" score 13 14 "METEIZKAYBE ISMLZKE S WMMSEH" score 12 15 "METEIZKAYBE ISMLZKE S WMMSEH" score 12 16 "METHIZKAYBE ISMLZKE S WMMSEH" score 11 17 "METHIZKAYBE ISMLZKE S WMASEH" score 10 18 "METHIZKAYBE ISMLZKE S WMASEH" score 10 [...] 67 "METHINKS RT ISMLIKE A WEASEL" score 2 68 "METHINKS RT ISMLIKE A WEASEL" score 2 69 "METHINKS RT ISMLIKE A WEASEL" score 2 70 "METHINKS RT ISMLIKE A WEASEL" score 2 71 "METHINKS RT ISMLIKE A WEASEL" score 2 72 "METHINKS RT IS LIKE A WEASEL" score 1 73 "METHINKS RT IS LIKE A WEASEL" score 1 74 "METHINKS RT IS LIKE A WEASEL" score 1 75 "METHINKS IT IS LIKE A WEASEL" score 0
Elena
ELENA 6.x :
import system'routines;
import extensions;
import extensions'text;
const string Target = "METHINKS IT IS LIKE A WEASEL";
const string AllowedCharacters = " ABCDEFGHIJKLMNOPQRSTUVWXYZ";
const int C = 100;
const real P = 0.05r;
rnd = randomGenerator;
randomChar
= AllowedCharacters[rnd.nextInt(AllowedCharacters.Length)];
extension evoHelper
{
randomString()
= 0.repeatTill(self).selectBy::(x => randomChar).summarize(new StringWriter());
fitnessOf(s)
= self.zipBy(s, (a,b => a==b ? 1 : 0)).summarize(new Integer()).toInt();
mutate(p)
= self.selectBy::(ch => rnd.nextReal() <= p ? randomChar : ch).summarize(new StringWriter());
}
class EvoAlgorithm : Enumerator
{
object _target;
object _current;
object _variantCount;
constructor new(s,count)
{
_target := s;
_variantCount := count.toInt();
}
get Value() = _current;
bool next()
{
if (nil == _current)
{ _current := _target.Length.randomString(); ^ true };
if (_target == _current)
{ ^ false };
auto variants := Array.allocate(_variantCount).populate::(x => _current.mutate(P) );
_current := variants.sort::(a,b => a.fitnessOf(Target) > b.fitnessOf(Target) ).at(0);
^ true
}
reset()
{
_current := nil
}
enumerable() => _target;
}
public program()
{
var attempt := new Integer();
EvoAlgorithm.new(Target,C).forEach::(current)
{
console
.printPaddingLeft(10,"#",attempt.append(1))
.printLine(" ",current," fitness: ",current.fitnessOf(Target))
};
console.readChar()
}- Output:
#1 WYHOOITVJKCPTOOTEVZJUNLCFDCV fitness: 0
#2 WYHOOITV KCPTOOTEVZJUNLCFDCV fitness: 1
#3 WYHOOITS KCPTOCTEVZ UNLCFDCV fitness: 3
#4 WYHO ITS KCPTO TEVZ UELCFDCV fitness: 4
#5 WYGO ITS DC ZO TEVZ UELCFDCV fitness: 5
#6 WYGO ITS DC ZO TEVZ UELCADCV fitness: 6
#7 WYGO ITS DT ZO TEVZ UELCADCV fitness: 7
#8 WYGOIITS DT ZO TEVZ LELCADRV fitness: 8
#9 WYGOIITS DT ZO TEVZ LELCADRL fitness: 9
#10 WYTOIITS HT ZZ TEVZ LEQCADRL fitness: 10
#11 WYTOIITS HT ZZ IEKZ LEQCADRL fitness: 11
#12 WYTOIITS HT ZZ IEKZ LEQCADEL fitness: 12
#13 WYTOIITS HT ZZ IEKZ LEQCASEL fitness: 13
#14 WYTOIIKS HT BZ IEKZ LEQCASEL fitness: 14
...
#34 METHINKS GT BS LGKE AEWGASEL fitness: 23
#35 METHINKS GT BS LIKE AEWGASEL fitness: 24
#36 METHINKS GT BS LIKE AEWGASEL fitness: 24
#37 METHINKS GT BS LIKE AEWGASEL fitness: 24
#38 METHINKS GT BS LIKE AEWGASEL fitness: 24
#39 METHINKS GT IS LIKE AEWYASEL fitness: 25
#40 METHINKS GT IS LIKE AEWYASEL fitness: 25
#41 METHINKS GT IS LIKE AEWEASEL fitness: 26
#42 METHINKS GT IS LIKE AEWEASEL fitness: 26
#43 METHINKS GT IS LIKE AEWEASEL fitness: 26
#44 METHINKS GT IS LIKE AEWEASEL fitness: 26
#45 METHINKS GT IS LIKE AEWEASEL fitness: 26
#46 METHINKS GT IS LIKE AEWEASEL fitness: 26
#47 METHINKS GT IS LIKE AEWEASEL fitness: 26
...
#57 METHINKS GT IS LIKE A WEASEL fitness: 27
#58 METHINKS GT IS LIKE A WEASEL fitness: 27
#59 METHINKS GT IS LIKE A WEASEL fitness: 27
#60 METHINKS GT IS LIKE A WEASEL fitness: 27
#61 METHINKS GT IS LIKE A WEASEL fitness: 27
#62 METHINKS GT IS LIKE A WEASEL fitness: 27
#63 METHINKS GT IS LIKE A WEASEL fitness: 27
#64 METHINKS LT IS LIKE A WEASEL fitness: 27
#65 METHINKS LT IS LIKE A WEASEL fitness: 27
#66 METHINKS LT IS LIKE A WEASEL fitness: 27
#67 METHINKS LT IS LIKE A WEASEL fitness: 27
#68 METHINKS LT IS LIKE A WEASEL fitness: 27
#69 METHINKS LT IS LIKE A WEASEL fitness: 27
#70 METHINKS LT IS LIKE A WEASEL fitness: 27
#71 METHINKS IT IS LIKE A WEASEL fitness: 28
Elixir
Print current gen and most fit offspring if more fit than parent.
Print the target and the total number of generations (iterations) it took to reach it.
defmodule Log do
def show(offspring,i) do
IO.puts "Generation: #{i}, Offspring: #{offspring}"
end
def found({target,i}) do
IO.puts "#{target} found in #{i} iterations"
end
end
defmodule Evolution do
# char list from A to Z; 32 is the ord value for space.
@chars [32 | Enum.to_list(?A..?Z)]
def select(target) do
(1..String.length(target)) # Creates parent for generation 0.
|> Enum.map(fn _-> Enum.random(@chars) end)
|> mutate(to_charlist(target),0)
|> Log.found
end
# w is used to denote fitness in population genetics.
defp mutate(parent,target,i) when target == parent, do: {parent,i}
defp mutate(parent,target,i) do
w = fitness(parent,target)
prev = reproduce(target,parent,mu_rate(w))
# Check if the most fit member of the new gen has a greater fitness than the parent.
if w < fitness(prev,target) do
Log.show(prev,i)
mutate(prev,target,i+1)
else
mutate(parent,target,i+1)
end
end
# Generate 100 offspring and select the one with the greatest fitness.
defp reproduce(target,parent,rate) do
[parent | (for _ <- 1..100, do: mutation(parent,rate))]
|> Enum.max_by(fn n -> fitness(n,target) end)
end
# Calculate fitness by checking difference between parent and offspring chars.
defp fitness(t,r) do
Enum.zip(t,r)
|> Enum.reduce(0, fn {tn,rn},sum -> abs(tn - rn) + sum end)
|> calc
end
# Generate offspring based on parent.
defp mutation(p,r) do
# Copy the parent chars, then check each val against the random mutation rate
Enum.map(p, fn n -> if :rand.uniform <= r, do: Enum.random(@chars), else: n end)
end
defp calc(sum), do: 100 * :math.exp(sum/-10)
defp mu_rate(n), do: 1 - :math.exp(-(100-n)/400)
end
Evolution.select("METHINKS IT IS LIKE A WEASEL")
- Output:
Generation: 0, Offspring: AFOSPRRLTLF CQKYFIGUMEUVBLRN Generation: 1, Offspring: HFOMJRRESLL FQKYQRGUM UVBLRN Generation: 2, Offspring: HFOMCRLIDLL FDKYQRGNM UVBLIN Generation: 3, Offspring: HFOMCOLIDQL FDKYQRG M UVBLIP Generation: 4, Offspring: HFOMCOLVLRL FD YYRG M UEBLIP Generation: 5, Offspring: HFOMCOLVLRL FS YYNH M UEBXJP Generation: 6, Offspring: KFOMCOLVLRL FS YYNH C UEBXJP Generation: 7, Offspring: EFOFCOCVLFT FV YCNH C UEBMJP Generation: 8, Offspring: EFWFCOCV FTBFV YCSH C UEBMJP Generation: 9, Offspring: EFWFJOCZ FTBRV DCMH C UEBMJP Generation: 11, Offspring: PFSFJOCL FVBRV DCMH C UEBJJP Generation: 12, Offspring: PFSDJYCL LV RK DKMH C UEBJJR Generation: 13, Offspring: IFSDJYCP LV MK DKMH C UEBSJR Generation: 14, Offspring: IFSDJTIP LV MK DKMH C UEBSGR Generation: 15, Offspring: IFSDJTIO JV MK SKMH C UEBSGR Generation: 16, Offspring: IFSKIJIO JV MK DKMH C UEBSGG Generation: 19, Offspring: IFSJIJIP JV MK DKIH C UEBSGH Generation: 20, Offspring: IFSJIJIP JV MO DMIH C UEBSGH Generation: 21, Offspring: IFWJDJIP JV IO EHJH C UEBSGH Generation: 23, Offspring: IFWJDJIP JV IO SHJH A XEBSGH Generation: 25, Offspring: IFWJDJIP JV IO SHJC A XEBSGH Generation: 26, Offspring: IFWJKJIP JV IO LHJC A XEBOGH Generation: 34, Offspring: IFTJKJIT JV IO LHJC A XEBOGH Generation: 39, Offspring: IFTJKOIT JV IO LHJC A XEBOGH Generation: 53, Offspring: IETJKOIT JV IO LHJC A XEBOGH Generation: 60, Offspring: IETJKOIT JV IO LHJC A XEBOEG Generation: 64, Offspring: IETJKOIT JV IO LHJF A XEBOEG Generation: 68, Offspring: LETGKOIT JV IO LHJF A XEBOEG Generation: 70, Offspring: LETGKOIT JV IS LHJF A XEBOBG Generation: 76, Offspring: LETEKOIT JV IS LHJF A XEBOBN Generation: 83, Offspring: LETHKOIT JV IS LHJF A XEBOFN Generation: 90, Offspring: LBTHKOIT JV IS LHJF A XEBSFN Generation: 92, Offspring: LBTHKOIT JV IS LHJF A XEBSFL Generation: 93, Offspring: LBTHKOJT JV IS LHJF A XEBSFL Generation: 123, Offspring: LETHKOJT JV IS LHJF A XEBSFL Generation: 125, Offspring: LETHHOJT JV IS LHJF A XEBSFL Generation: 135, Offspring: LETHHOJT JV IS LIJF A XEBSFL Generation: 143, Offspring: LETHHOJT IV IS LIJF A XEBSFL Generation: 161, Offspring: LETHHNJT IV IS LIJF A XEBSFL Generation: 165, Offspring: METHHNJT IV IS LIJF A XEBSFL Generation: 169, Offspring: METHHNKT IV IS LIJF A XEBSFL Generation: 171, Offspring: METHHNKT IV IS LIJE A XEBSFL Generation: 175, Offspring: METHHNKT IS IS LIJE A XEBSFL Generation: 213, Offspring: METHHNKT IS IS LIKE A XEBSFL Generation: 218, Offspring: METHINKT IS IS LIKE A XEBSFL Generation: 234, Offspring: METHINKT IS IS LIKE A XEBSEL Generation: 237, Offspring: METHINKT IS IS LIKE A XEASEL Generation: 241, Offspring: METHINKT IS IS LIKE A WEASEL Generation: 243, Offspring: METHINKT IT IS LIKE A WEASEL Generation: 247, Offspring: METHINKS IT IS LIKE A WEASEL METHINKS IT IS LIKE A WEASEL found in 248 iterations
Erlang
-module(evolution).
-export([run/0]).
-define(MUTATE, 0.05).
-define(POPULATION, 100).
-define(TARGET, "METHINKS IT IS LIKE A WEASEL").
-define(MAX_GENERATIONS, 1000).
run() -> evolve_gens().
evolve_gens() ->
Initial = random_string(length(?TARGET)),
evolve_gens(Initial,0,fitness(Initial)).
evolve_gens(Parent,Generation,0) ->
io:format("Generation[~w]: Achieved the target: ~s~n",[Generation,Parent]);
evolve_gens(Parent,Generation,_Fitness) when Generation == ?MAX_GENERATIONS ->
io:format("Reached Max Generations~nFinal string is ~s~n",[Parent]);
evolve_gens(Parent,Generation,Fitness) ->
io:format("Generation[~w]: ~s, Fitness: ~w~n",
[Generation,Parent,Fitness]),
Child = evolve_string(Parent),
evolve_gens(Child,Generation+1,fitness(Child)).
fitness(String) -> fitness(String, ?TARGET).
fitness([],[]) -> 0;
fitness([H|Rest],[H|Target]) -> fitness(Rest,Target);
fitness([_H|Rest],[_T|Target]) -> 1+fitness(Rest,Target).
mutate(String) -> mutate(String,[]).
mutate([],Acc) -> lists:reverse(Acc);
mutate([H|T],Acc) ->
case random:uniform() < ?MUTATE of
true ->
mutate(T,[random_character()|Acc]);
false ->
mutate(T,[H|Acc])
end.
evolve_string(String) ->
evolve_string(String,?TARGET,?POPULATION,String).
evolve_string(_,_,0,Child) -> Child;
evolve_string(Parent,Target,Population,Best_Child) ->
Child = mutate(Parent),
case fitness(Child) < fitness(Best_Child) of
true ->
evolve_string(Parent,Target,Population-1,Child);
false ->
evolve_string(Parent,Target,Population-1,Best_Child)
end.
random_character() ->
case random:uniform(27)-1 of
26 -> $ ;
R -> $A+R
end.
random_string(Length) -> random_string(Length,[]).
random_string(0,Acc) -> Acc;
random_string(N,Acc) when N > 0 ->
random_string(N-1,[random_character()|Acc]).
Euphoria
constant table = "ABCDEFGHIJKLMNOPQRSTUVWXYZ "
function random_generation(integer len)
sequence s
s = rand(repeat(length(table),len))
for i = 1 to len do
s[i] = table[s[i]]
end for
return s
end function
function mutate(sequence s, integer n)
for i = 1 to length(s) do
if rand(n) = 1 then
s[i] = table[rand(length(table))]
end if
end for
return s
end function
function fitness(sequence probe, sequence target)
atom sum
sum = 0
for i = 1 to length(target) do
sum += power(find(target[i], table) - find(probe[i], table), 2)
end for
return sqrt(sum/length(target))
end function
constant target = "METHINKS IT IS LIKE A WEASEL", C = 30, MUTATE = 15
sequence parent, specimen
integer iter, best
atom fit, best_fit
parent = random_generation(length(target))
iter = 0
while not equal(parent,target) do
best_fit = fitness(parent, target)
printf(1,"Iteration: %3d, \"%s\", deviation %g\n", {iter, parent, best_fit})
specimen = repeat(parent,C+1)
best = C+1
for i = 1 to C do
specimen[i] = mutate(specimen[i], MUTATE)
fit = fitness(specimen[i], target)
if fit < best_fit then
best_fit = fit
best = i
end if
end for
parent = specimen[best]
iter += 1
end while
printf(1,"Finally, \"%s\"\n",{parent})Output:
Iteration: 0, "HRGPWKOOARZL KTJEBPUYPTOLGDK", deviation 11.1002 Iteration: 1, "HRGPWKOOWRZLLKTJEBPUYPTOLGDK", deviation 9.40175 Iteration: 2, "HRGPOKOOWRZVLKTJEBPUYPTOLGDK", deviation 8.69113 Iteration: 3, "HRKPOKOOWRZVLKTJEBPUDPTOLGDB", deviation 7.46181 Iteration: 4, "HEKPOKOOWRZVLKTJEBPUDPTOLGDB", deviation 7.04577 Iteration: 5, "HEKPOKOOWRZVLKTJEBEUDPTOLGDB", deviation 6.73212 Iteration: 6, "HEKPOKOOWRZVLKTJEBEUDPTALGDB", deviation 6.50549 Iteration: 7, "HEKPOKOOWIZVLKTJEBEUDPTALGDB", deviation 6.27922 Iteration: 8, "HESPOKOOWIZVLKTJEBEUDPTALJDB", deviation 5.85845 Iteration: 9, "HESPOKOOWIZVLKTJEBEUIPTALJDJ", deviation 5.73212 ... Iteration: 201, "METHINKS IT IT LIKE A WEASEL", deviation 0.188982 Iteration: 202, "METHINKS IT IT LIKE A WEASEL", deviation 0.188982 Iteration: 203, "METHINKS IT IT LIKE A WEASEL", deviation 0.188982 Iteration: 204, "METHINKS IT IT LIKE A WEASEL", deviation 0.188982 Iteration: 205, "METHINKS IT IT LIKE A WEASEL", deviation 0.188982 Iteration: 206, "METHINKS IT IT LIKE A WEASEL", deviation 0.188982 Iteration: 207, "METHINKS IT IT LIKE A WEASEL", deviation 0.188982 Iteration: 208, "METHINKS IT IT LIKE A WEASEL", deviation 0.188982 Iteration: 209, "METHINKS IT IT LIKE A WEASEL", deviation 0.188982 Iteration: 210, "METHINKS IT IT LIKE A WEASEL", deviation 0.188982 Finally, "METHINKS IT IS LIKE A WEASEL"
F#
//A functional implementation of Evolutionary algorithm
//Nigel Galloway February 7th., 2018
let G=System.Random 23
let fitness n=Array.fold2(fun a n g->if n=g then a else a+1) 0 n ("METHINKS IT IS LIKE A WEASEL".ToCharArray())
let alphabet="QWERTYUIOPASDFGHJKLZXCVBNM ".ToCharArray()
let mutate (n:char[]) g=Array.iter(fun g->n.[g]<-alphabet.[G.Next()%27]) (Array.init g (fun _->G.Next()%(Array.length n)));n
let nextParent n g=List.init 500 (fun _->mutate (Array.copy n) g)|>List.minBy fitness
let evolution n=let rec evolution n g=match fitness n with |0->(0,n)::g |l->evolution (nextParent n ((l/2)+1)) ((l,n)::g)
evolution n []
let n = evolution (Array.init 28 (fun _->alphabet.[G.Next()%27]))
Real: 00:00:00.021, CPU: 00:00:00.050, GC gen0: 1, gen1: 0
Length of n (37) is the number of generations including the original parent as follows:
Length of n (37) is the number of generations including the original parent as follows: (28, [|' '; 'V'; 'L'; 'D'; 'N'; 'Q'; 'A'; 'Z'; 'P'; 'A'; 'J'; 'A'; 'T'; 'C'; 'S'; 'I'; 'G'; 'H'; 'M'; 'Q'; 'M'; 'J'; 'Y'; 'L'; 'Q'; 'H'; 'S'; 'A'|]) (25, [|'D'; 'V'; 'L'; 'B'; 'N'; 'S'; 'A'; 'Z'; 'B'; 'A'; 'J'; 'Y'; 'T'; 'M'; 'U'; 'L'; 'G'; 'M'; 'M'; 'Q'; 'M'; ' '; 'Y'; 'L'; 'Q'; S'; 'X'; 'Y'|]) (23, [|'V'; 'E'; 'L'; 'A'; 'N'; 'S'; 'A'; 'Z'; 'P'; 'A'; 'J'; ' '; 'T'; 'M'; 'L'; 'L'; 'G'; 'D'; 'M'; 'Z'; 'S'; ' '; 'A'; 'L'; 'L'; 'S'; 'X'; 'Y'|]) (21, [|'V'; 'S'; 'L'; 'J'; 'N'; 'S'; 'A'; 'S'; 'P'; 'A'; 'J'; ' '; ' '; 'M'; 'L'; 'L'; 'G'; 'D'; 'E'; 'I'; 'A'; ' '; 'A'; 'L'; 'L'; 'S'; 'X'; 'Y'|]) (20, [|'V'; 'S'; 'E'; 'H'; 'N'; ' '; 'A'; 'S'; 'P'; 'S'; 'J'; ' '; 'Z'; 'P'; 'L'; 'L'; 'G'; 'B'; 'E'; 'Y'; 'A'; ' '; 'D'; 'H'; 'V'; 'S'; 'X'; 'Y'|]) (18, [|'V'; 'S'; 'K'; 'H'; 'N'; ' '; 'K'; 'S'; 'M'; 'S'; 'J'; ' '; 'I'; 'P'; 'V'; 'L'; 'D'; 'B'; 'E'; 'Y'; 'A'; ' '; 'X'; 'J'; 'V'; 'S'; 'X'; 'Y'|]) (16, [|'W'; 'S'; 'K'; 'H'; 'N'; ' '; 'K'; 'S'; 'M'; 'S'; 'D'; ' '; 'I'; 'S'; 'V'; 'L'; 'D'; 'T'; 'E'; ' '; 'A'; ' '; 'C'; 'J'; 'V'; 'S'; 'W'; 'Y'|]) (14, [|'W'; 'E'; 'K'; 'H'; 'X'; 'G'; 'K'; 'S'; 'M'; 'H'; 'D'; ' '; 'I'; 'S'; 'V'; 'L'; ' '; 'T'; 'E'; ' '; 'A'; ' '; 'C'; 'J'; 'R'; 'S'; 'W'; 'L'|]) (14, [|'W'; 'E'; 'E'; 'H'; 'I'; 'L'; 'K'; 'S'; 'M'; 'H'; 'D'; 'W'; 'I'; 'S'; 'O'; 'L'; 'M'; 'A'; 'E'; ' '; 'A'; ' '; 'Q'; 'J'; 'R'; 'S'; 'W'; 'L'|]) (13, [|'W'; 'E'; 'E'; 'H'; 'I'; 'L'; 'K'; 'S'; 'M'; 'H'; 'D'; 'W'; 'I'; 'S'; 'R'; 'L'; 'S'; 'A'; 'E'; ' '; 'A'; ' '; 'Q'; 'J'; 'Z'; 'S'; 'E'; 'L'|]) (12, [|'W'; 'E'; 'E'; 'H'; 'I'; 'L'; 'K'; 'S'; 'M'; 'H'; 'D'; 'O'; 'I'; 'S'; 'C'; 'L'; 'I'; 'Y'; 'E'; ' '; 'A'; ' '; 'J'; 'O'; 'R'; 'S'; 'E'; 'L'|]) (10, [|'B'; 'E'; 'A'; 'H'; 'I'; 'N'; 'K'; 'S'; 'M'; 'C'; 'T'; 'O'; 'I'; 'S'; 'C'; 'L'; 'I'; 'R'; 'E'; ' '; 'A'; ' '; 'J'; 'O'; 'R'; 'S'; 'E'; 'L'|]) (9, [|'M'; 'E'; 'A'; 'H'; 'I'; 'N'; 'K'; 'S'; 'N'; 'C'; 'T'; 'F'; 'I'; 'S'; 'C'; 'L'; 'I'; 'R'; 'E'; ' '; 'A'; ' '; 'K'; 'N'; 'R'; 'S'; 'E'; 'L'|]) (9, [|'M'; 'E'; 'A'; 'H'; 'I'; 'N'; 'K'; 'S'; 'T'; 'P'; 'T'; 'F'; 'I'; 'S'; 'C'; 'L'; 'I'; 'R'; 'E'; ' '; 'A'; ' '; 'K'; 'N'; 'P'; 'S'; 'E'; 'L'|]) (8, [|'M'; 'E'; 'N'; 'H'; 'I'; 'N'; 'K'; 'S'; 'L'; 'P'; 'T'; 'F'; 'I'; 'S'; 'Y'; 'L'; 'I'; 'K'; 'E'; ' '; 'A'; ' '; 'K'; 'H'; 'P'; 'S'; 'E'; 'L'|]) (8, [|'M'; 'E'; 'N'; 'H'; 'I'; 'N'; 'K'; 'S'; 'L'; 'E'; 'T'; 'F'; 'I'; 'R'; 'Y'; 'L'; 'I'; 'K'; 'E'; ' '; 'A'; ' '; 'Q'; 'H'; 'A'; 'S'; 'E'; 'L'|]) (7, [|'M'; 'E'; ' '; 'H'; 'I'; 'N'; 'K'; 'S'; ' '; 'E'; 'T'; 'F'; 'I'; 'K'; 'Y'; 'L'; 'I'; 'K'; 'E'; ' '; 'A'; ' '; 'Q'; 'H'; 'A'; 'S'; 'E'; 'L'|]) (7, [|'M'; 'E'; ' '; 'H'; 'I'; 'N'; 'K'; 'S'; ' '; 'E'; 'T'; 'F'; 'I'; 'K'; 'J'; 'L'; 'I'; 'K'; 'E'; ' '; 'A'; ' '; 'Q'; 'H'; 'A'; 'S'; 'E'; 'L'|]) (6, [|'M'; 'E'; 'T'; 'H'; 'I'; 'N'; 'K'; 'S'; ' '; 'I'; 'T'; 'F'; 'I'; 'K'; 'J'; 'L'; 'I'; 'D'; 'E'; ' '; 'A'; ' '; 'Q'; 'Z'; 'A'; 'S'; 'E'; 'L'|]) (5, [|'M'; 'E'; 'T'; 'H'; 'I'; 'N'; 'K'; 'S'; ' '; 'I'; 'T'; ' '; 'I'; 'E'; 'J'; 'L'; 'I'; 'T'; 'E'; ' '; 'A'; ' '; 'X'; 'Z'; 'A'; 'S'; 'E'; 'L'|]) (5, [|'M'; 'E'; 'T'; 'H'; 'I'; 'F'; 'K'; 'S'; ' '; 'I'; 'T'; ' '; 'I'; 'S'; 'I'; 'L'; 'I'; 'T'; 'E'; ' '; 'A'; ' '; 'X'; 'Z'; 'A'; 'S'; 'E'; 'L'|]) (5, [|'M'; 'E'; 'T'; 'H'; 'I'; 'F'; 'K'; 'S'; ' '; 'I'; 'T'; ' '; 'I'; 'S'; ' '; 'L'; 'I'; 'T'; 'E'; ' '; 'A'; ' '; 'K'; 'Z'; 'A'; 'Z'; 'E'; 'L'|]) (5, [|'M'; 'E'; 'T'; 'H'; 'I'; 'F'; 'K'; 'S'; ' '; 'I'; 'T'; ' '; 'I'; 'S'; ' '; 'L'; 'I'; 'F'; 'E'; ' '; 'A'; ' '; 'K'; 'Z'; 'A'; 'P'; 'E'; 'L'|]) (5, [|'M'; 'E'; 'T'; 'H'; 'I'; 'R'; 'K'; 'S'; ' '; 'I'; 'T'; ' '; 'I'; 'S'; ' '; 'L'; 'I'; 'F'; 'E'; ' '; 'A'; ' '; 'K'; 'Z'; 'A'; 'F'; 'E'; 'L'|]) (4, [|'M'; 'E'; 'T'; 'H'; 'I'; 'N'; 'K'; 'S'; ' '; 'I'; 'T'; ' '; 'I'; 'S'; ' '; 'L'; 'I'; 'F'; 'E'; ' '; 'A'; ' '; 'K'; 'Z'; 'A'; 'F'; 'E'; 'L'|]) (3, [|'M'; 'E'; 'T'; 'H'; 'I'; 'N'; 'K'; 'S'; ' '; 'I'; 'T'; ' '; 'I'; 'S'; ' '; 'L'; 'I'; 'J'; 'E'; ' '; 'A'; ' '; 'K'; 'E'; 'A'; 'F'; 'E'; 'L'|]) (3, [|'M'; 'E'; 'T'; 'H'; 'I'; 'N'; 'K'; 'S'; ' '; 'I'; 'T'; ' '; 'I'; 'S'; ' '; 'L'; 'I'; 'J'; 'E'; ' '; 'A'; ' '; 'Y'; 'E'; 'A'; 'F'; 'E'; 'L'|]) (3, [|'M'; 'E'; 'T'; 'H'; 'I'; 'N'; 'K'; 'S'; ' '; 'I'; 'T'; ' '; 'I'; 'S'; ' '; 'L'; 'I'; 'J'; 'E'; ' '; 'A'; ' '; 'K'; 'E'; 'A'; 'G'; 'E'; 'L'|]) (3, [|'M'; 'E'; 'T'; 'H'; 'I'; 'N'; 'K'; 'S'; ' '; 'I'; 'T'; ' '; 'I'; 'S'; ' '; 'L'; 'I'; 'A'; 'E'; ' '; 'A'; ' '; 'K'; 'E'; 'A'; 'G'; 'E'; 'L'|]) (2, [|'M'; 'E'; 'T'; 'H'; 'I'; 'N'; 'K'; 'S'; ' '; 'I'; 'T'; ' '; 'I'; 'S'; ' '; 'L'; 'I'; 'K'; 'E'; ' '; 'A'; ' '; 'K'; 'E'; 'A'; 'Q'; 'E'; 'L'|]) (2, [|'M'; 'E'; 'T'; 'H'; 'I'; 'N'; 'K'; 'S'; ' '; 'I'; 'T'; ' '; 'I'; 'S'; ' '; 'L'; 'I'; 'K'; 'E'; ' '; 'A'; ' '; 'K'; 'E'; 'A'; 'Q'; 'E'; 'L'|]) (2, [|'M'; 'E'; 'T'; 'H'; 'I'; 'N'; 'K'; 'S'; ' '; 'I'; 'T'; ' '; 'I'; 'S'; ' '; 'L'; 'I'; 'K'; 'E'; ' '; 'A'; ' '; 'K'; 'E'; 'A'; 'N'; 'E'; 'L'|]) (1, [|'M'; 'E'; 'T'; 'H'; 'I'; 'N'; 'K'; 'S'; ' '; 'I'; 'T'; ' '; 'I'; 'S'; ' '; 'L'; 'I'; 'K'; 'E'; ' '; 'A'; ' '; 'K'; 'E'; 'A'; 'S'; 'E'; 'L'|]) (1, [|'M'; 'E'; 'T'; 'H'; 'I'; 'N'; 'K'; 'S'; ' '; 'I'; 'T'; ' '; 'I'; 'S'; ' '; 'L'; 'I'; 'K'; 'E'; ' '; 'A'; ' '; 'K'; 'E'; 'A'; 'S'; 'E'; 'L'|]) (1, [|'M'; 'E'; 'T'; 'H'; 'I'; 'N'; 'K'; 'S'; ' '; 'I'; 'T'; ' '; 'I'; 'S'; ' '; 'L'; 'I'; 'K'; 'E'; ' '; 'A'; ' '; 'N'; 'E'; 'A'; 'S'; 'E'; 'L'|]) (1, [|'M'; 'E'; 'T'; 'H'; 'I'; 'N'; 'K'; 'S'; ' '; 'I'; 'T'; ' '; 'I'; 'S'; ' '; 'L'; 'I'; 'K'; 'E'; ' '; 'A'; ' '; 'H'; 'E'; 'A'; 'S'; 'E'; 'L'|]) (0, [|'M'; 'E'; 'T'; 'H'; 'I'; 'N'; 'K'; 'S'; ' '; 'I'; 'T'; ' '; 'I'; 'S'; ' '; 'L'; 'I'; 'K'; 'E'; ' '; 'A'; ' '; 'W'; 'E'; 'A'; 'S'; 'E'; 'L'|])
Factor
USING: arrays formatting io kernel literals math prettyprint
random sequences strings ;
FROM: math.extras => ... ;
IN: rosetta-code.evolutionary-algorithm
CONSTANT: target "METHINKS IT IS LIKE A WEASEL"
CONSTANT: mutation-rate 0.1
CONSTANT: num-children 25
CONSTANT: valid-chars
$[ CHAR: A ... CHAR: Z >array { 32 } append ]
: rand-char ( -- n )
valid-chars random ;
: new-parent ( -- str )
target length [ rand-char ] replicate >string ;
: fitness ( str -- n )
target [ = ] { } 2map-as sift length ;
: mutate ( str rate -- str/str' )
[ random-unit > [ drop rand-char ] when ] curry map ;
: next-parent ( str -- str/str' )
dup [ mutation-rate mutate ] curry num-children 1 - swap
replicate [ 1array ] dip append [ fitness ] supremum-by ;
: print-parent ( str -- )
[ fitness pprint bl ] [ print ] bi ;
: main ( -- )
0 new-parent
[ dup target = ]
[ next-parent dup print-parent [ 1 + ] dip ] until drop
"Finished in %d generations." printf ;
MAIN: main
- Output:
1 JWTBPZMHKOFFWDSBCLZUCFUAWUJ 2 JWTAPFMSKOFFWDSBCLZUCHUAWUJ 3 JWTAPSOSKOFFWDOBFLZ CHGAWUJ ... 14 MWTTISKS EFFWS LIKE JZGAWBKL ... 28 METHINKS IT IS LIKE A WEASEL Finished in 298 generations.
Fantom
class Main
{
static const Str target := "METHINKS IT IS LIKE A WEASEL"
static const Int C := 100 // size of population
static const Float p := 0.1f // chance any char is mutated
// compute distance of str from target
static Int fitness (Str str)
{
Int sum := 0
str.each |Int c, Int index|
{
if (c != target[index]) sum += 1
}
return sum
}
// mutate given parent string
static Str mutate (Str str)
{
Str result := ""
str.size.times |Int index|
{
result += ((Float.random < p) ? randomChar() : str[index]).toChar
}
return result
}
// return a random char
static Int randomChar ()
{
"ABCDEFGHIJKLMNOPQRSTUVWXYZ "[Int.random(0..26)]
}
// make population by mutating parent and sorting by fitness
static Str[] makePopulation (Str parent)
{
Str[] result := [,]
C.times { result.add (mutate(parent)) }
result.sort |Str a, Str b -> Int| { fitness(a) <=> fitness(b) }
return result
}
public static Void main ()
{
Str parent := ""
target.size.times { parent += randomChar().toChar }
while (parent != target)
{
echo (parent)
parent = makePopulation(parent).first
}
echo (parent)
}
}Forth
include lib/choose.4th
\ target string
s" METHINKS IT IS LIKE A WEASEL" sconstant target
27 constant /charset \ size of characterset
29 constant /target \ size of target string
32 constant #copies \ number of offspring
/target string charset \ characterset
/target string this-generation \ current generation and offspring
/target #copies [*] string new-generation
:this new-generation does> swap /target chars * + ;
\ generate a mutation
: mutation charset /charset choose chars + c@ ;
\ print the current candidate
: .candidate ( n1 n2 -- n1 f)
." Generation " over 2 .r ." : " this-generation count type cr /target -1 [+] =
; \ test a candidate on
\ THE NUMBER of correct genes
: test-candidate ( a -- a n)
dup target 0 >r >r ( a1 a2)
begin ( a1 a2)
r@ ( a1 a2 n)
while ( a1 a2)
over c@ over c@ = ( a1 a2 n)
r> r> rot if 1+ then >r 1- >r ( a1 a2)
char+ swap char+ swap ( a1+1 a2+1)
repeat ( a1+1 a2+1)
drop drop r> drop r> ( a n)
;
\ find the best candidate
: get-candidate ( -- n)
#copies 0 >r >r ( --)
begin ( --)
r@ ( n)
while ( --)
r@ 1- new-generation ( a)
test-candidate r'@ over < ( a n f)
if swap count this-generation place r> 1- swap r> drop >r >r
else drop drop r> 1- >r then ( --)
repeat ( --)
r> drop r> ( n)
;
\ generate a new candidate
: make-candidate ( a --)
dup charset count rot place ( a1)
this-generation target >r ( a1 a2 a3)
begin ( a1 a2 a3)
r@ ( a1 a2 a3 n)
while ( a1 a2 a3)
over c@ over c@ = ( a1 a2 a3 f)
swap >r >r over r> ( a1 a2 a1 f)
if over c@ else mutation then ( a1 a2 a1 c)
swap c! r> r> 1- >r ( a1 a2 a3)
char+ rot char+ rot char+ rot ( a1+1 a2+1 a3+1)
repeat ( a1+1 a2+1 a3+1)
drop drop drop r> drop ( --)
;
\ make a whole new generation
: make-generation #copies 0 do i new-generation make-candidate loop ;
\ weasel program
: weasel
s" ABCDEFGHIJKLMNOPQRSTUVWXYZ " 2dup
charset place \ initialize the characterset
this-generation place 0 \ initialize the first generation
begin \ start the program
1+ make-generation \ make a new generation
get-candidate .candidate \ select the best candidate
until drop \ stop when we've found perfection
;
weasel
Output:
habe@linux-471m:~> 4th cxq weasel1.4th Generation 1: MUPHMOOXEIBGELPUZZEGXIVMELFL Generation 2: MUBHIYDPKIQWYXSVLUEBH TYJMRL Generation 3: MEVHIUTZDIVQSMRT KEDP GURBSL Generation 4: MEWHIHKPKITBWSYVYKEXZ ASBAL Generation 5: MEVHIPKMRIT VSTSBKE R YNJWEL Generation 6: MERHIIKQ IT OSNEUKE A TKCLEL Generation 7: METHINKO IT SXREKE A JDAIEL Generation 8: METHINKS IT SSSVIKE A OIA EL Generation 9: METHINKS IT ISICIKE A IGASEL Generation 10: METHINKS IT ISITIKE A WZASEL Generation 11: METHINKS IT ISACIKE A WEASEL Generation 12: METHINKS IT ISKLIKE A WEASEL Generation 13: METHINKS IT IS LIKE A WEASEL
Fortran
!***************************************************************************************************
module evolve_routines
!***************************************************************************************************
implicit none
!the target string:
character(len=*),parameter :: targ = 'METHINKS IT IS LIKE A WEASEL'
contains
!***************************************************************************************************
!********************************************************************
pure elemental function fitness(member) result(n)
!********************************************************************
! The fitness function. The lower the value, the better the match.
! It is zero if they are identical.
!********************************************************************
implicit none
integer :: n
character(len=*),intent(in) :: member
integer :: i
n=0
do i=1,len(targ)
n = n + abs( ichar(targ(i:i)) - ichar(member(i:i)) )
end do
!********************************************************************
end function fitness
!********************************************************************
!********************************************************************
pure elemental subroutine mutate(member,factor)
!********************************************************************
! mutate a member of the population.
!********************************************************************
implicit none
character(len=*),intent(inout) :: member !population member
real,intent(in) :: factor !mutation factor
integer,parameter :: n_chars = 27 !number of characters in set
character(len=n_chars),parameter :: chars = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ '
real :: rnd_val
integer :: i,j,n
n = len(member)
do i=1,n
rnd_val = rand()
if (rnd_val<=factor) then !mutate this element
rnd_val = rand()
j = int(rnd_val*n_chars)+1 !an integer between 1 and n_chars
member(i:i) = chars(j:j)
end if
end do
!********************************************************************
end subroutine mutate
!********************************************************************
!***************************************************************************************************
end module evolve_routines
!***************************************************************************************************
!***************************************************************************************************
program evolve
!***************************************************************************************************
! The main program
!***************************************************************************************************
use evolve_routines
implicit none
!Tuning parameters:
integer,parameter :: seed = 12345 !random number generator seed
integer,parameter :: max_iter = 10000 !maximum number of iterations
integer,parameter :: population_size = 200 !size of the population
real,parameter :: factor = 0.04 ![0,1] mutation factor
integer,parameter :: iprint = 5 !print every iprint iterations
!local variables:
integer :: i,iter
integer,dimension(1) :: i_best
character(len=len(targ)),dimension(population_size) :: population
!initialize random number generator:
call srand(seed)
!create initial population:
! [the first element of the population will hold the best member]
population(1) = 'PACQXJB CQPWEYKSVDCIOUPKUOJY' !initial guess
iter=0
write(*,'(A10,A30,A10)') 'iter','best','fitness'
write(*,'(I10,A30,I10)') iter,population(1),fitness(population(1))
do
iter = iter + 1 !iteration counter
!write the iteration:
if (mod(iter,iprint)==0) write(*,'(I10,A30,I10)') iter,population(1),fitness(population(1))
!check exit conditions:
if ( iter>max_iter .or. fitness(population(1))==0 ) exit
!copy best member and mutate:
population = population(1)
do i=2,population_size
call mutate(population(i),factor)
end do
!select the new best population member:
! [the best has the lowest value]
i_best = minloc(fitness(population))
population(1) = population(i_best(1))
end do
!write the last iteration:
if (mod(iter,iprint)/=0) write(*,'(I10,A30,I10)') iter,population(1),fitness(population(1))
if (iter>max_iter) then
write(*,*) 'No solution found.'
else
write(*,*) 'Solution found.'
end if
!***************************************************************************************************
end program evolve
!***************************************************************************************************
The output is:
iter best fitness
0 PACQXJB CQPWEYKSVDCIOUPKUOJY 459
5 PACDXJBRCQP EYKSVDK OAPKGOJY 278
10 PAPDJJBOCQP EYCDKDK A PHGQJF 177
15 PAUDJJBO FP FY VKBL A PEGQJF 100
20 PEUDJMOO KP FY IKLD A YECQJF 57
25 PEUHJMOT KU FS IKLD A YECQJL 35
30 PEUHJMIT KU GS LKJD A YEAQFL 23
35 MERHJMIT KT IS LHJD A YEASFL 15
40 MERHJMKS IT IS LIJD A WEASFL 7
45 MERHINKS IT IS LIJD A WEASFL 5
50 MERHINKS IT IS LIJD A WEASEL 4
55 MERHINKS IT IS LIKD A WEASEL 3
60 MESHINKS IT IS LIKD A WEASEL 2
65 MESHINKS IT IS LIKD A WEASEL 2
70 MESHINKS IT IS LIKE A WEASEL 1
75 METHINKS IT IS LIKE A WEASEL 0
FreeBASIC
' version 01-07-2018
' compile with: fbc -s console
Randomize Timer
Const As UInteger children = 100
Const As Double mutate_rate = 0.05
Function fitness(target As String, tmp As String) As UInteger
Dim As UInteger x, f
For x = 0 To Len(tmp) -1
If tmp[x] = target[x] Then f += 1
Next
Return f
End Function
Sub mutate(tmp As String, chars As String, mute_rate As Double)
If Rnd <= mute_rate Then
tmp[Int(Rnd * Len(tmp))] = chars[Int(Rnd * Len(chars))]
End If
End Sub
' ------=< MAIN >=------
Dim As String target = "METHINKS IT IS LIKE A WEASEL"
Dim As String chars = " ABCDEFGHIJKLMNOPQRSTUVWXYZ"
Dim As String parent, mutation()
Dim As UInteger x, iter, f, fit(), best_fit, parent_fit
For x = 1 To Len(target)
parent += Chr(chars[Int(Rnd * Len(chars))])
Next
f = fitness(target, parent)
parent_fit = f
best_fit = f
Print "iteration best fit Parent"
Print "========= ======== ============================"
Print Using " #### #### ";iter; best_fit;
Print parent
Do
iter += 1
ReDim mutation(1 To children),fit(1 To children)
For x = 1 To children
mutation(x) = parent
mutate(mutation(x), chars, mutate_rate)
Next
For x = 1 To children
If mutation(x) <> parent Then
f = fitness(target, mutation(x))
If best_fit < f Then
best_fit = f
fit(x) = f
Else
fit(x) = parent_fit
End If
End If
Next
If best_fit > parent_fit Then
For x = 1 To children
If fit(x) = best_fit Then
parent = mutation(x)
Print Using " #### #### ";iter; best_fit;
Print parent
End If
Next
End If
Loop Until parent = target
' empty keyboard buffer
While InKey <> "" : Wend
Print : Print "hit any key to end program"
Sleep
End
- Output:
iteration best fit Parent
========= ======== ============================
0 2 VDHQATVSHHSVRFNAPFEGZARZGCZE
3 3 VEHQATVSHHSVRFNAPFEGZARZGCZE
5 4 VEHQATVSHHSVRFNAPFEGZAREGCZE
11 5 VEHQATKSHHSVRFNAPFEGZAREGCZE
19 6 VEHQATKSHHSVRFNAPFEGZAREGSZE
32 7 VEHQANKSHHSVRFNAPFEGZAREGSZE
36 8 VEHQANKSHHSVRFNAPFEGAAREGSZE
38 9 VEHQANKSHHTVRFNAPFEGAAREGSZE
39 10 VEHQANKSHHTVRFNAPFEGAAREGSEE
48 11 VEHHANKSHHTVRFNAPFEGAAREGSEE
53 12 VEHHANKSHITVRFNAPFEGAAREGSEE
73 13 VEHHINKSHITVRFNAPFEGAAREGSEE
81 14 VEHHINKSHITVRFNAPFEGAAWEGSEE
95 15 VEHHINKSHITVIFNAPFEGAAWEGSEE
96 16 VEHHINKSHITVIFNLPFEGAAWEGSEE
135 17 VETHINKSHITVIFNLPFEGAAWEGSEE
137 18 VETHINKSHITVISNLPFEGAAWEGSEE
152 19 VETHINKSHITVISNLPKEGAAWEGSEE
171 20 VETHINKSHITVISNLPKEGAAWEGSEL
174 21 VETHINKSHITVIS LPKEGAAWEGSEL
188 22 VETHINKSHITVIS LIKEGAAWEGSEL
213 23 VETHINKSHIT IS LIKEGAAWEGSEL
220 24 METHINKSHIT IS LIKEGAAWEGSEL
374 25 METHINKSHIT IS LIKE AAWEGSEL
378 26 METHINKSHIT IS LIKE A WEGSEL
555 27 METHINKS IT IS LIKE A WEGSEL
585 28 METHINKS IT IS LIKE A WEASEL
Fōrmulæ
Fōrmulæ programs are not textual, visualization/edition of programs is done showing/manipulating structures but not text. Moreover, there can be multiple visual representations of the same program. Even though it is possible to have textual representation —i.e. XML, JSON— they are intended for storage and transfer purposes more than visualization and edition.
Programs in Fōrmulæ are created/edited online in its website.
In this page you can see and run the program(s) related to this task and their results. You can also change either the programs or the parameters they are called with, for experimentation, but remember that these programs were created with the main purpose of showing a clear solution of the task, and they generally lack any kind of validation.
Solution
Test case
A graph of the fitness function
Go
I took the liberty to use []byte for the "strings" mentioned in the task description. Go has a native string type, but in this case it was both easier and more efficient to work with byte slices and just convert to string when there was something to print.
package main
import (
"fmt"
"math/rand"
"time"
)
var target = []byte("METHINKS IT IS LIKE A WEASEL")
var set = []byte("ABCDEFGHIJKLMNOPQRSTUVWXYZ ")
var parent []byte
func init() {
rand.Seed(time.Now().UnixNano())
parent = make([]byte, len(target))
for i := range parent {
parent[i] = set[rand.Intn(len(set))]
}
}
// fitness: 0 is perfect fit. greater numbers indicate worse fit.
func fitness(a []byte) (h int) {
// (hamming distance)
for i, tc := range target {
if a[i] != tc {
h++
}
}
return
}
// set m to mutation of p, with each character of p mutated with probability r
func mutate(p, m []byte, r float64) {
for i, ch := range p {
if rand.Float64() < r {
m[i] = set[rand.Intn(len(set))]
} else {
m[i] = ch
}
}
}
func main() {
const c = 20 // number of times to copy and mutate parent
copies := make([][]byte, c)
for i := range copies {
copies[i] = make([]byte, len(parent))
}
fmt.Println(string(parent))
for best := fitness(parent); best > 0; {
for _, cp := range copies {
mutate(parent, cp, .05)
}
for _, cp := range copies {
fm := fitness(cp)
if fm < best {
best = fm
copy(parent, cp)
fmt.Println(string(parent))
}
}
}
}
- Output:
HRVDKMXETOIOVSFMVHWKIY ZDXEY HRVDKMXE OIOVSFMVHWKIY ZDWEY HRVDKMXE OIOISFMVHWVIY ZDSEY HRVDKMXE OIOISFMFHWVI ZDSEL HRVDKMXE OIOISFLFHWVI ZDSEL HRVDKMXE OIOISFLFHWVI ZASEL HRVDKMXS OIOISFLFHWVI ZASEL HRVHKMXS OIOISFLHHWVI ZASEL MRVHKMXS OHOISFLHHWVI ZASEL MRVHKMXS OTOISFLHHWVI FASEL MRVHKNXS OTOISFLHHWVI FASEL MRVHKNXS OTOISFLHHWVI EASEL MEVHKNXS OTOISFLHHWVI IEASEL MEVHKNXS OTOISFLHHWVI WEASEL METHKNXS OTOISFLHHWVI WEASEL METHKNXS ZTOIS LHHWVI WEASEL METHKNKS ZTOIS LHHWVI WEASEL METHKNKS ZTOIS LHKWEI WEASEL METHKNKS ZT IS LHKWEI WEASEL METHKNKS ZT IS LHKEEI WEASEL METHKNKS ZT IS LHKEEA WEASEL METHKNKS ZT IS LHKE A WEASEL METHKNKS ZT IS LIKE A WEASEL METHINKS ZT IS LIKE A WEASEL METHINKS IT IS LIKE A WEASEL
Haskell
import System.Random
import Control.Monad
import Data.List
import Data.Ord
import Data.Array
showNum :: (Num a, Show a) => Int -> a -> String
showNum w = until ((>w-1).length) (' ':) . show
replace :: Int -> a -> [a] -> [a]
replace n c ls = take (n-1) ls ++ [c] ++ drop n ls
target = "METHINKS IT IS LIKE A WEASEL"
pfit = length target
mutateRate = 20
popsize = 100
charSet = listArray (0,26) $ ' ': ['A'..'Z'] :: Array Int Char
fitness = length . filter id . zipWith (==) target
printRes i g = putStrLn $
"gen:" ++ showNum 4 i ++ " "
++ "fitn:" ++ showNum 4 (round $ 100 * fromIntegral s / fromIntegral pfit ) ++ "% "
++ show g
where s = fitness g
mutate :: [Char] -> Int -> IO [Char]
mutate g mr = do
let r = length g
chances <- replicateM r $ randomRIO (1,mr)
let pos = elemIndices 1 chances
chrs <- replicateM (length pos) $ randomRIO (bounds charSet)
let nchrs = map (charSet!) chrs
return $ foldl (\ng (p,c) -> replace (p+1) c ng) g (zip pos nchrs)
evolve :: [Char] -> Int -> Int -> IO ()
evolve parent gen mr = do
when ((gen-1) `mod` 20 == 0) $ printRes (gen-1) parent
children <- replicateM popsize (mutate parent mr)
let child = maximumBy (comparing fitness) (parent:children)
if fitness child == pfit then printRes gen child
else evolve child (succ gen) mr
main = do
let r = length target
genes <- replicateM r $ randomRIO (bounds charSet)
let parent = map (charSet!) genes
evolve parent 1 mutateRate
Example run in GHCi:
*Main> main gen: 0 fitn: 4% "AICJEWXYSFTMOAYOHNFZ HSLFNBY" gen: 20 fitn: 54% "XZTHIWXSSVTMSUYOIKEZA WEFSEL" gen: 40 fitn: 89% "METHINXSSIT IS OIKE A WEASEL" gen: 60 fitn: 93% "METHINXSSIT IS LIKE A WEASEL" gen: 78 fitn: 100% "METHINKS IT IS LIKE A WEASEL"
Alternate Presentation
I find this easier to read.
import System.Random
import Data.List
import Data.Ord
import Data.Array
import Control.Monad
import Control.Arrow
target = "METHINKS IT IS LIKE A WEASEL"
mutateRate = 0.1
popSize = 100
printEvery = 10
alphabet = listArray (0,26) (' ':['A'..'Z'])
randomChar = (randomRIO (0,26) :: IO Int) >>= return . (alphabet !)
origin = mapM createChar target
where createChar c = randomChar
fitness = length . filter id . zipWith (==) target
mutate = mapM mutateChar
where mutateChar c = do
r <- randomRIO (0.0,1.0) :: IO Double
if r < mutateRate then randomChar else return c
converge n parent = do
if n`mod`printEvery == 0 then putStrLn fmtd else return ()
if target == parent
then putStrLn $ "\nFinal: " ++ fmtd
else mapM mutate (replicate (popSize-1) parent) >>=
converge (n+1) . fst . maximumBy (comparing snd) . map (id &&& fitness) . (parent:)
where fmtd = parent ++ ": " ++ show (fitness parent) ++ " (" ++ show n ++ ")"
main = origin >>= converge 0
Example:
YUZVNNZ SXPSNGZFRHZKVDOEPIGS: 2 (0) BEZHANK KIPONSYSPKV F AEULEC: 11 (10) BETHANKSFIT ISYHIKJ I TERLER: 17 (20) METHINKS IT IS YIKE R TERYER: 22 (30) METHINKS IT IS YIKE WEASEQ: 25 (40) METHINKS IT IS MIKE WEASEI: 25 (50) METHINKS IT IS LIKE D WEASEI: 26 (60) METHINKS IT IS LIKE T WEASEX: 26 (70) METHINKS IT IS LIKE I WEASEL: 27 (80) Final: METHINKS IT IS LIKE A WEASEL: 28 (86)
Icon and Unicon
Insitux
Define the evolution parameters (values here per Wikipedia article), with a couple of problem constants.
(var c 100) ;number of children in each generation
(var p 0.05) ;mutation probability
(var target "METHINKS IT IS LIKE A WEASEL")
(var tsize (len target))
(var alphabet (to-vec " ABCDEFGHIJLKLMNOPQRSTUVWXYZ"))Now the major functions. fitness simply counts the number of characters matching the target.
(var fitness (comp @(map = target) (count val)))
(var perfect-fit? (comp fitness (= tsize)))
(var rand-char #(rand-pick alphabet))
(var mutate (map #(if (< (rand) p) (rand-char) %)))Finally evolve. At each generation, print the generation number, the parent, and the parent's fitness.
(function evolve generation parent
(print (pad-left " " 3 generation) " " (... str parent) " " (fitness parent))
(return-when (perfect-fit? parent))
(let children (times c #(mutate parent))
fittest (max-by fitness (... vec parent children)))
(recur (inc generation) fittest))
(evolve 1 (times tsize rand-char))- Output:
1 LUUDSLXITIQ JREKRDIQFXBPJMBA 2 2 LUUDSLXITIQ LREKRDIQFXBPHMEA 3 ... 10 LUFRSLKSTIQ CFHLIGANB WPHMEL 10 11 LUFRSLKSTIQ CF LIGANB WPHMEL 11 ... 20 MUTRINKSAIT GF LITENB WPDSEL 18 21 METRINKSAIT NY LITENB WPDSEL 19 ... 30 METWINKS IT N LITE B WEASEL 23 31 METWINKS IT N LITE B WEASEL 23 ... 40 METHINKS IT IS LITE A WEASEL 27 41 METHINKS IT IS LITE A WEASEL 27 ... 50 METHINKS IT IS LITE A WEASEL 27 51 METHINKS IT IS LIKE A WEASEL 28
J
Solution:
Using sum of differences from the target for fitness, i.e. 0 is optimal fitness.
CHARSET=: 'ABCDEFGHIJKLMNOPQRSTUVWXYZ '
NPROG=: 100 NB. number of progeny (C)
MRATE=: 0.05 NB. mutation rate
create =: (?@$&$ { ])&CHARSET NB. creates random list from charset of same shape as y
fitness =: +/@:~:"1
copy =: # ,:
mutate =: &(>: $ ?@$ 0:)(`(,: create))} NB. adverb
select =: ] {~ (i. <./)@:fitness NB. select fittest member of population
nextgen =: select ] , [: MRATE mutate NPROG copy ]
while =: conjunction def '(] , (u {:))^:(v {:)^:_ ,:'
evolve=: nextgen while (0 < fitness) create
Example usage:
Returns list of best solutions at each generation until converged.
filter=: {: ,~ ({~ i.@>.&.(%&20)@#) NB. take every 20th and last item
filter evolve 'METHINKS IT IS LIKE A WEASEL'
XXURVQXKQXDLCGFVICCUA NUQPND
MEFHINVQQXT IW LIKEUA WEAPEL
METHINVS IT IW LIKEUA WEAPEL
METHINKS IT IS LIKE A WEASEL
Alternative solution:
Using explicit versions of mutate and evolve above.
CHARSET=: 'ABCDEFGHIJKLMNOPQRSTUVWXYZ '
NPROG=: 100 NB. "C" from specification
fitness=: +/@:~:"1
select=: ] {~ (i. <./)@:fitness NB. select fittest member of population
populate=: (?@$&# { ])&CHARSET NB. get random list from charset of same length as y
log=: [: smoutput [: ;:inv (('#';'fitness: ';'; ') ,&.> ":&.>)
mutate=: dyad define
idxmut=. I. x >: (*/$y) ?@$ 0
(populate idxmut) idxmut"_} y
)
evolve=: monad define
target=. y
parent=. populate y
iter=. 0
mrate=. %#y
while. 0 < val=. target fitness parent do.
if. 0 = 50|iter do. log iter;val;parent end.
iter=. iter + 1
progeny=. mrate mutate NPROG # ,: parent NB. create progeny by mutating parent copies
parent=. target select parent,progeny NB. select fittest parent for next generation
end.
log iter;val;parent
parent
)
Example Usage:
evolve 'METHINKS IT IS LIKE A WEASEL'
#0 fitness: 27 ; YGFDJFTBEDB FAIJJGMFKDPYELOA
#50 fitness: 2 ; MEVHINKS IT IS LIKE ADWEASEL
#76 fitness: 0 ; METHINKS IT IS LIKE A WEASEL
METHINKS IT IS LIKE A WEASEL
Java
(Close)
import java.util.Random;
public class EvoAlgo {
static final String target = "METHINKS IT IS LIKE A WEASEL";
static final char[] possibilities = "ABCDEFGHIJKLMNOPQRSTUVWXYZ ".toCharArray();
static int C = 100; //number of spawn per generation
static double minMutateRate = 0.09;
static int perfectFitness = target.length();
private static String parent;
static Random rand = new Random();
private static int fitness(String trial){
int retVal = 0;
for(int i = 0;i < trial.length(); i++){
if (trial.charAt(i) == target.charAt(i)) retVal++;
}
return retVal;
}
private static double newMutateRate(){
return (((double)perfectFitness - fitness(parent)) / perfectFitness * (1 - minMutateRate));
}
private static String mutate(String parent, double rate){
String retVal = "";
for(int i = 0;i < parent.length(); i++){
retVal += (rand.nextDouble() <= rate) ?
possibilities[rand.nextInt(possibilities.length)]:
parent.charAt(i);
}
return retVal;
}
public static void main(String[] args){
parent = mutate(target, 1);
int iter = 0;
while(!target.equals(parent)){
double rate = newMutateRate();
iter++;
if(iter % 100 == 0){
System.out.println(iter +": "+parent+ ", fitness: "+fitness(parent)+", rate: "+rate);
}
String bestSpawn = null;
int bestFit = 0;
for(int i = 0; i < C; i++){
String spawn = mutate(parent, rate);
int fitness = fitness(spawn);
if(fitness > bestFit){
bestSpawn = spawn;
bestFit = fitness;
}
}
parent = bestFit > fitness(parent) ? bestSpawn : parent;
}
System.out.println(parent+", "+iter);
}
}
Output:
100: MEVHIBXSCG TP QIK FZGJ SEL, fitness: 13, rate: 0.4875 200: MEBHINMSVI IHTQIKW FTDEZSWL, fitness: 15, rate: 0.42250000000000004 300: METHINMSMIA IHUFIKA F WEYSEL, fitness: 19, rate: 0.29250000000000004 400: METHINSS IT IQULIKA F WEGSEL, fitness: 22, rate: 0.195 METHINKS IT IS LIKE A WEASEL, 492
JavaScript
Using cross-browser techniques to support Array.reduce and Array.map
// ------------------------------------- Cross-browser Compatibility -------------------------------------
/* Compatibility code to reduce an array
* Source: https://developer.mozilla.org/en/JavaScript/Reference/Global_Objects/Array/Reduce
*/
if (!Array.prototype.reduce) {
Array.prototype.reduce = function (fun /*, initialValue */ ) {
"use strict";
if (this === void 0 || this === null) throw new TypeError();
var t = Object(this);
var len = t.length >>> 0;
if (typeof fun !== "function") throw new TypeError();
// no value to return if no initial value and an empty array
if (len == 0 && arguments.length == 1) throw new TypeError();
var k = 0;
var accumulator;
if (arguments.length >= 2) {
accumulator = arguments[1];
} else {
do {
if (k in t) {
accumulator = t[k++];
break;
}
// if array contains no values, no initial value to return
if (++k >= len) throw new TypeError();
}
while (true);
}
while (k < len) {
if (k in t) accumulator = fun.call(undefined, accumulator, t[k], k, t);
k++;
}
return accumulator;
};
}
/* Compatibility code to map an array
* Source: https://developer.mozilla.org/en/JavaScript/Reference/Global_Objects/Array/Map
*/
if (!Array.prototype.map) {
Array.prototype.map = function (fun /*, thisp */ ) {
"use strict";
if (this === void 0 || this === null) throw new TypeError();
var t = Object(this);
var len = t.length >>> 0;
if (typeof fun !== "function") throw new TypeError();
var res = new Array(len);
var thisp = arguments[1];
for (var i = 0; i < len; i++) {
if (i in t) res[i] = fun.call(thisp, t[i], i, t);
}
return res;
};
}
/* ------------------------------------- Generator -------------------------------------
* Generates a fixed length gene sequence via a gene strategy object.
* The gene strategy object must have two functions:
* - "create": returns create a new gene
* - "mutate(existingGene)": returns mutation of an existing gene
*/
function Generator(length, mutationRate, geneStrategy) {
this.size = length;
this.mutationRate = mutationRate;
this.geneStrategy = geneStrategy;
}
Generator.prototype.spawn = function () {
var genes = [],
x;
for (x = 0; x < this.size; x += 1) {
genes.push(this.geneStrategy.create());
}
return genes;
};
Generator.prototype.mutate = function (parent) {
return parent.map(function (char) {
if (Math.random() > this.mutationRate) {
return char;
}
return this.geneStrategy.mutate(char);
}, this);
};
/* ------------------------------------- Population -------------------------------------
* Helper class that holds and spawns a new population.
*/
function Population(size, generator) {
this.size = size;
this.generator = generator;
this.population = [];
// Build initial popuation;
for (var x = 0; x < this.size; x += 1) {
this.population.push(this.generator.spawn());
}
}
Population.prototype.spawn = function (parent) {
this.population = [];
for (var x = 0; x < this.size; x += 1) {
this.population.push(this.generator.mutate(parent));
}
};
/* ------------------------------------- Evolver -------------------------------------
* Attempts to converge a population based a fitness strategy object.
* The fitness strategy object must have three function
* - "score(individual)": returns a score for an individual.
* - "compare(scoreA, scoreB)": return true if scoreA is better (ie more fit) then scoreB
* - "done( score )": return true if score is acceptable (ie we have successfully converged).
*/
function Evolver(size, generator, fitness) {
this.done = false;
this.fitness = fitness;
this.population = new Population(size, generator);
}
Evolver.prototype.getFittest = function () {
return this.population.population.reduce(function (best, individual) {
var currentScore = this.fitness.score(individual);
if (best === null || this.fitness.compare(currentScore, best.score)) {
return {
score: currentScore,
individual: individual
};
} else {
return best;
}
}, null);
};
Evolver.prototype.doGeneration = function () {
this.fittest = this.getFittest();
this.done = this.fitness.done(this.fittest.score);
if (!this.done) {
this.population.spawn(this.fittest.individual);
}
};
Evolver.prototype.run = function (onCheckpoint, checkPointFrequency) {
checkPointFrequency = checkPointFrequency || 10; // Default to Checkpoints every 10 generations
var generation = 0;
while (!this.done) {
this.doGeneration();
if (generation % checkPointFrequency === 0) {
onCheckpoint(generation, this.fittest);
}
generation += 1;
}
onCheckpoint(generation, this.fittest);
return this.fittest;
};
// ------------------------------------- Exports -------------------------------------
window.Generator = Generator;
window.Evolver = Evolver;
// helper utitlity to combine elements of two arrays.
Array.prototype.zip = function (b, func) {
var result = [],
max = Math.max(this.length, b.length),
x;
for (x = 0; x < max; x += 1) {
result.push(func(this[x], b[x]));
}
return result;
};
var target = "METHINKS IT IS LIKE A WEASEL", geneStrategy, fitness, target, generator, evolver, result;
geneStrategy = {
// The allowed character set (as an array)
characterSet: "ABCDEFGHIJKLMNOPQRSTUVWXYZ ".split(""),
/*
Pick a random character from the characterSet
*/
create: function getRandomGene() {
var randomNumber = Math.floor(Math.random() * this.characterSet.length);
return this.characterSet[randomNumber];
}
};
geneStrategy.mutate = geneStrategy.create; // Our mutation stragtegy is to simply get a random gene
fitness = {
// The target (as an array of characters)
target: target.split(""),
equal: function (geneA, geneB) {
return (geneA === geneB ? 0 : 1);
},
sum: function (runningTotal, value) {
return runningTotal + value;
},
/*
We give one point to for each corect letter
*/
score: function (genes) {
var diff = genes.zip(this.target, this.equal); // create an array of ones and zeros
return diff.reduce(this.sum, 0); // Sum the array values together.
},
compare: function (scoreA, scoreB) {
return scoreA <= scoreB; // Lower scores are better
},
done: function (score) {
return score === 0; // We have matched the target string.
}
};
generator = new Generator(target.length, 0.05, geneStrategy);
evolver = new Evolver(100, generator, fitness);
function showProgress(generation, fittest) {
document.write("Generation: " + generation + ", Best: " + fittest.individual.join("") + ", fitness:" + fittest.score + "<br>");
}
result = evolver.run(showProgress);
Output:
Generation: 0, Best: KSTFOKJC XZYLWCLLGYZJNXYEGHE, fitness:25 Generation: 10, Best: KOTFINJC XX LS LIGYZT WEPSHL, fitness:14 Generation: 20, Best: KBTHINKS BT LS LIGNZA WEPSEL, fitness:8 Generation: 30, Best: KETHINKS IT BS LISNZA WEASEL, fitness:5 Generation: 40, Best: KETHINKS IT IS LIKEZA WEASEL, fitness:2 Generation: 50, Best: METHINKS IT IS LIKEZA WEASEL, fitness:1 Generation: 52, Best: METHINKS IT IS LIKE A WEASEL, fitness:0
An attempt using ES6
by A.K.Bateman 2023
"use strict"
const TARGET = "METHINKS IT IS LIKE A WEASEL";
const GENE_POOL = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ ';
const C = 100;
const MUTATION_RATE = 0.3;
function randomIdGenerator(length) {
return Math.floor(Math.random() * length);
}
function getGene() {
return GENE_POOL[randomIdGenerator(GENE_POOL.length)];
}
class Parent {
_arrayLength;
_genePool;
_geneGenerator;
constructor(arrayLength, genePool, geneGenerator) {
if (typeof arrayLength === 'number' && arrayLength > 0) {
this._arrayLength = arrayLength;
}
if (typeof genePool === 'string' && genePool.length > 0) {
this._genePool = [...genePool];
}
if (typeof geneGenerator === 'function') {
this._geneGenerator = geneGenerator;
}
}
generate() {
const letters = [];
while (letters.length < this._arrayLength) {
letters.push(this._geneGenerator());
}
return letters.join('');
}
}
function fitness(needle, target) {
if (needle.length !== target.length) return 0;
const needleArray = [...needle];
let count = 0;
[...target].forEach((item, index) => {
if (item === needleArray[index]) count++;
});
return count;
}
function mutate({ source, mutationRate }) {
if (typeof source !== 'string' || source.length === 0) return '';
const sourceLength = source.length;
const iterations = Math.floor(sourceLength * mutationRate);
const stringArray = [...source];
for(let i = 0; i < iterations; i++) {
const shouldReplace = Boolean(Math.floor(Math.random() * 2));
if(shouldReplace) {
const id = randomIdGenerator(sourceLength);
stringArray[id] = getGene();
}
}
return stringArray.join('');
}
function createMutants(parent, mutantNumber) {
const mutantArray = [];
for (let i = 0; i < mutantNumber; i++) {
const mutant = mutate({source: parent, mutationRate: MUTATION_RATE});
mutantArray.push(
{
mutant,
fitness: fitness(mutant, TARGET),
},
);
}
return mutantArray;
}
function helperInit(parentString, parentFitness) {
const mutant = createMutants(parentString, C)
.sort( (a,b) => a.fitness - b.fitness ).pop();
if (mutant.fitness >= parentFitness) {
return {
string: mutant.mutant,
fitness: mutant.fitness,
};
}
return {
string: parentString,
fitness: parentFitness,
};
}
function run() {
const parent = new Parent(TARGET.length, GENE_POOL, getGene);
let parentString = parent.generate();
let parentFitness = fitness(parentString, TARGET);
let genCount = 0;
while(parentString !== TARGET) {
const init = helperInit(parentString, parentFitness);
parentString = init.string;
parentFitness = init.fitness;
console.log(init.string);
genCount++;
}
console.log(`Ended in ${genCount} generations`);
}
run();
example output:
METHYNKS IT IS LIKE A WEASEL METHYNKS IT IS LIKE A WEASEL METHYNKS IT IS LIKE A WEASEL METHYNKS IT IS LIKE A WEASEL METHYNKS IT IS LIKE A WEASEL METHINKS IT IS LIKE A WEASEL Ended in 240 generations
jq
Works with gojq, the Go implementation of jq(*)
Works with jaq, the Rust implementation of jq(*)
In this entry, the "fitness" score is based on codepoint differences; this has the effect of preserving correctly located spaces. Notice also how the algorithm quickly achieves an approximate solution but takes a while to arrive at the destination.
Since jq currently does not have a PRNG, the following assumes the availability of /dev/random as a source of entropy. The output shown below was generated by invoking jq in the pipeline:
< /dev/random tr -cd '0-9' | fold -w 3 | $JQ -cnr -f evolutionary-algorithm.jq
(*) For gojq and jaq, leading 0s must be stripped from the input, e.g. by `sed -e '/^0./s/0//' -e '/^0./s/0//'`.
# Assumption: input consists of random three-digit numbers i.e. 000 to 999
def rand: input;
def set: "ABCDEFGHIJKLMNOPQRSTUVWXYZ ";
def abs:
if . < 0 then -. else . end;
def ichar:
if type == "number" then . else explode[0] end;
# Output: a pseudo-random character from set.
# $n should be a random number drawn from range(0; N) inclusive where N > set|length
# Input: an admissible character from `set` (ignored in this implementation)
def shift($n):
($n % (set|length)) as $i
| set[$i:$i+1];
# fitness: 0 indicates a perfect fit; greater numbers indicate worse fit.
def fitness($gold):
def diff($c; $d): ($c|ichar) - ($d|ichar) | abs;
. as $in
| reduce range(0;length) as $i (0; . + diff($in[$i:$i+1]; $gold[$i:$i+1]));
# Input: a string
# Output: a mutation of . such that each character is mutated with probability $r
def mutate($r):
# Output: a pseudo-random character from set
# $n should be a random number drawn from range(0; N) inclusive where N > set|length
def letter($n):
($n % (set|length)) as $i
| set[$i:$i+1];
. as $p
| reduce range(0;length) as $i ("";
rand as $rand
| if ($rand/1000) < $r then . + letter($rand)
else . + $p[$i:$i+1]
end );
# An array of $n children of the parent provided as input; $r is the mutation probability
def children($n; $r):
[range(0;$n) as $i | mutate($r)];
# Input: a "parent"
# Output: a single string
def next_generation($gold; $r):
([.] + children(100; $r))
| min_by( fitness($gold) );
# Evolve towards the target string provided as input, using $r as the mutation rate;
# `recurse` is used in order to show progress conveniently.
def evolve($r):
. as $gold
| (set|length) as $s
| (reduce range(0; $n) as $i (""; (rand % $s) as $j | . + set[$j:$j+1])) as $string
| {count: 0, $string }
| recurse (
if .string | fitness($gold) == 0 then empty
else .string |= next_generation($gold; $r)
| .count += 1
end);
"METHINKS IT IS LIKE A WEASEL" | evolve(0.05)- Output:
{"count":0,"string":"OIRSP AVNMTSSKOEIRBGDXZUBIQL"}
{"count":1,"string":"OIRSP AV MTSSKOEIRBGDXZUBIQL"}
{"count":2,"string":"OIRSP AV MTSAKOEIRBGD ZUBIQL"}
{"count":3,"string":"OIRSPCAV MTSAWOEIRBGD ZUBIQL"}
{"count":4,"string":"OIRJACAV MTEAWOEIRBGD ZUBIQL"}
{"count":5,"string":"OIRJACTV MTEAWOEIRBGD ZGBIQL"}
{"count":6,"string":"OIRJBCTV MTEAWAEIRBGD ZGBIJL"}
{"count":7,"string":"OIRJBCTV MTEAWAEIRBGD ZGBTJL"}
{"count":8,"string":"UIRJBCTV MT AWFEIRBGG ZGBTJL"}
{"count":9,"string":"UIRJBMTV MT AWFEIRBGG ZGBTJL"}
{"count":10,"string":"JIRJEMTV MT AWFEIKGGG ZGBTJL"}
{"count":11,"string":"JIRJEMKV MT AWFEIKGGG XGBTJL"}
{"count":12,"string":"JIRJEMKV MT AWFEIKGGG XGBTAL"}
{"count":13,"string":"JIRJEMKV MT KWFEIKGGG XGBTAL"}
{"count":14,"string":"JFRJEMKP MT KWFEIKGGG XGBTAL"}
{"count":15,"string":"JFRJEMKP MT KWFJIKGGG XGBTAL"}
{"count":16,"string":"JFRJEMKT MT KQFJIKGGG XGBTAL"}
{"count":17,"string":"JFRJEMKT MT HQFJIKGGG XGBTAL"}
{"count":18,"string":"JFRJEMKT MT HQFJIKGGC XGBTAL"}
{"count":19,"string":"LFRJEMKT MT HQFJIIGGC XGATFL"}
{"count":20,"string":"LFRJIMKT MT HQFJIIGGC XGATFL"}
...
{"count":61,"string":"MFTHINKS JT IR LIKE A WEAREL"}
{"count":62,"string":"MFTHINKS JT IR LIKE A WEAREL"}
{"count":63,"string":"MFTHINKS JT IR LIKE A WEAREL"}
{"count":64,"string":"MFTHINKS JT IR LIKE A WEAREL"}
{"count":65,"string":"MFTHINKS JT IR LIKE A WEAREL"}
{"count":66,"string":"MFTHINKS JT IR LIKE A WEAREL"}
{"count":67,"string":"MFTHINKS JT IR LIKE A WEAREL"}
{"count":68,"string":"MFTHINKS JT IR LIKE A WEAREL"}
{"count":69,"string":"MFTHINKS IT IR LIKE A WEAREL"}
{"count":70,"string":"MFTHINKS IT IR LIKE A WEAREL"}
{"count":71,"string":"MFTHINKS IT IS LIKE A WEAREL"}
{"count":72,"string":"MFTHINKS IT IS LIKE A WEAREL"}
{"count":73,"string":"MFTHINKS IT IS LIKE A WEAREL"}
{"count":74,"string":"MFTHINKS IT IS LIKE A WEAREL"}
{"count":75,"string":"MFTHINKS IT IS LIKE A WEAREL"}
{"count":76,"string":"METHINKS IT IS LIKE A WEAREL"}
{"count":77,"string":"METHINKS IT IS LIKE A WEAREL"}
{"count":78,"string":"METHINKS IT IS LIKE A WEAREL"}
{"count":79,"string":"METHINKS IT IS LIKE A WEAREL"}
{"count":80,"string":"METHINKS IT IS LIKE A WEAREL"}
{"count":81,"string":"METHINKS IT IS LIKE A WEAREL"}
{"count":82,"string":"METHINKS IT IS LIKE A WEASEL"}
Julia
fitness(a::AbstractString, b::AbstractString) = count(l == t for (l, t) in zip(a, b))
function mutate(str::AbstractString, rate::Float64)
L = collect(Char, " ABCDEFGHIJKLMNOPQRSTUVWXYZ")
return map(str) do c
if rand() < rate rand(L) else c end
end
end
function evolve(parent::String, target::String, mutrate::Float64, nchild::Int)
println("Initial parent is $parent, its fitness is $(fitness(parent, target))")
gens = 0
while parent != target
children = collect(mutate(parent, mutrate) for i in 1:nchild)
bestfit, best = findmax(fitness.(children, target))
parent = children[best]
gens += 1
if gens % 10 == 0
println("After $gens generations, the new parent is $parent and its fitness is $(fitness(parent, target))")
end
end
println("After $gens generations, the parent evolved into the target $target")
end
evolve("IU RFSGJABGOLYWF XSMFXNIABKT", "METHINKS IT IS LIKE A WEASEL", 0.08998, 100)
- Output:
Initial parent is IU RFSGJABGOLYWF XSMFXNIABKT, its fitness is 1 After 10 generations, the new parent is MOTBSNGTABTTIL LIXEMA WMALSN and its fitness is 13 After 20 generations, the new parent is METHINGATITUIS LIXE A WEASEQ and its fitness is 22 After 30 generations, the new parent is METHINKSLIT ISELIAE A WEASES and its fitness is 24 After 40 generations, the new parent is METHINKS IT IS LINE A WEASEL and its fitness is 27 After 50 generations, the new parent is METHINKS IT IS LINE A WEASEL and its fitness is 27 After 60 generations, the new parent is METHINKS IT IS PIKE A WEASEL and its fitness is 27 After 70 generations, the new parent is METHINKS IT IS AIKE A WEASEL and its fitness is 27 After 80 generations, the new parent is METHINKS IT IS AIKE A WEASEL and its fitness is 27 After 81 generations, the parent evolved into the target METHINKS IT IS LIKE A WEASEL
Kotlin
import java.util.*
val target = "METHINKS IT IS LIKE A WEASEL"
val validChars = "ABCDEFGHIJKLMNOPQRSTUVWXYZ "
val random = Random()
fun randomChar() = validChars[random.nextInt(validChars.length)]
fun hammingDistance(s1: String, s2: String) =
s1.zip(s2).map { if (it.first == it.second) 0 else 1 }.sum()
fun fitness(s1: String) = target.length - hammingDistance(s1, target)
fun mutate(s1: String, mutationRate: Double) =
s1.map { if (random.nextDouble() > mutationRate) it else randomChar() }
.joinToString(separator = "")
fun main(args: Array<String>) {
val initialString = (0 until target.length).map { randomChar() }.joinToString(separator = "")
println(initialString)
println(mutate(initialString, 0.2))
val mutationRate = 0.05
val childrenPerGen = 50
var i = 0
var currVal = initialString
while (currVal != target) {
i += 1
currVal = (0..childrenPerGen).map { mutate(currVal, mutationRate) }.maxBy { fitness(it) }!!
}
println("Evolution found target after $i generations")
}
Liberty BASIC
C = 10
'mutaterate has to be greater than 1 or it will not mutate
mutaterate = 2
mutationstaken = 0
generations = 0
Dim parentcopies$((C - 1))
Global targetString$ : targetString$ = "METHINKS IT IS LIKE A WEASEL"
Global allowableCharacters$ : allowableCharacters$ = " ABCDEFGHIJKLMNOPQRSTUVWXYZ"
currentminFitness = Len(targetString$)
For i = 1 To Len(targetString$)
parent$ = parent$ + Mid$(allowableCharacters$, Int(Rnd(1) + 1 * Len(allowableCharacters$)), 1) 'corrected line
Next i
Print "Parent = " + parent$
While parent$ <> targetString$
generations = (generations + 1)
For i = 0 To (C - 1)
parentcopies$(i) = mutate$(parent$, mutaterate)
mutationstaken = (mutationstaken + 1)
Next i
For i = 0 To (C - 1)
currentFitness = Fitness(targetString$, parentcopies$(i))
If currentFitness = 0 Then
parent$ = parentcopies$(i)
Exit For
Else
If currentFitness < currentminFitness Then
currentminFitness = currentFitness
parent$ = parentcopies$(i)
End If
End If
Next i
CLS
Print "Generation - " + str$(generations)
Print "Parent - " + parent$
Scan
Wend
Print
Print "Congratulations to me; I finished!"
Print "Final Mutation: " + parent$
'The ((i + 1) - (C)) reduces the total number of mutations that it took by one generation
'minus the perfect child mutation since any after that would not have been required.
Print "Total Mutations Taken - " + str$(mutationstaken - ((i + 1) - (C)))
Print "Total Generations Taken - " + str$(generations)
Print "Child Number " + str$(i) + " has perfect similarities to your target."
End
Function mutate$(mutate$, mutaterate)
If (Rnd(1) * mutaterate) > 1 Then
'The mutatingcharater randomizer needs 1 more than the length of the string
'otherwise it will likely take forever to get exactly that as a random number
mutatingcharacter = Int(Rnd(1) * (Len(targetString$) + 1))
mutate$ = Left$(mutate$, (mutatingcharacter - 1)) + Mid$(allowableCharacters$, Int(Rnd(1) * Len(allowableCharacters$)), 1) _
+ Mid$(mutate$, (mutatingcharacter + 1))
End If
End Function
Function Fitness(parent$, offspring$)
For i = 1 To Len(targetString$)
If Mid$(parent$, i, 1) <> Mid$(offspring$, i, 1) Then
Fitness = (Fitness + 1)
End If
Next i
End FunctionLogo
make "target "|METHINKS IT IS LIKE A WEASEL|
to distance :w
output reduce "sum (map.se [ifelse equal? ?1 ?2 [0][1]] :w :target)
end
to random.letter
output pick "| ABCDEFGHIJKLMNOPQRSTUVWXYZ|
end
to mutate :parent :rate
output map [ifelse random 100 < :rate [random.letter] [?]] :parent
end
make "C 100
make "mutate.rate 10 ; percent
to breed :parent
make "parent.distance distance :parent
localmake "best.child :parent
repeat :C [
localmake "child mutate :parent :mutate.rate
localmake "child.distance distance :child
if greater? :parent.distance :child.distance [
make "parent.distance :child.distance
make "best.child :child
]
]
output :best.child
end
to progress
output (sentence :trials :parent "distance: :parent.distance)
end
to evolve
make "parent cascade count :target [lput random.letter ?] "||
make "trials 0
while [not equal? :parent :target] [
make "parent breed :parent
print progress
make "trials :trials + 1
]
endLua
local target = "METHINKS IT IS LIKE A WEASEL"
local alphabet = "ABCDEFGHIJKLMNOPQRSTUVWXYZ "
local c, p = 100, 0.06
local function fitness(s)
local score = #target
for i = 1,#target do
if s:sub(i,i) == target:sub(i,i) then score = score - 1 end
end
return score
end
local function mutate(s, rate)
local result, idx = ""
for i = 1,#s do
if math.random() < rate then
idx = math.random(#alphabet)
result = result .. alphabet:sub(idx,idx)
else
result = result .. s:sub(i,i)
end
end
return result, fitness(result)
end
local function randomString(len)
local result, idx = ""
for i = 1,len do
idx = math.random(#alphabet)
result = result .. alphabet:sub(idx,idx)
end
return result
end
local function printStep(step, s, fit)
print(string.format("%04d: ", step) .. s .. " [" .. fit .."]")
end
math.randomseed(os.time())
local parent = randomString(#target)
printStep(0, parent, fitness(parent))
local step = 0
while parent ~= target do
local bestFitness, bestChild, child, fitness = #target + 1
for i = 1,c do
child, fitness = mutate(parent, p)
if fitness < bestFitness then bestFitness, bestChild = fitness, child end
end
parent, step = bestChild, step + 1
printStep(step, parent, bestFitness)
end
M2000 Interpreter
Version 1
Module WeaselAlgorithm {
Print "Evolutionary Algorithm"
\\ Weasel Algorithm
\\ Using dynamic array, which expand if no fitness change,
\\ and reduce to minimum when fitness changed
\\ Abandon strings when fitness change
\\ Also lambda function Mutate$ change when topscore=10, to change only one character
l$="ABCDEFGHIJKLMNOPQRSTUVWXYZ "
randomstring$=lambda$ l$ ->{
res$=""
For i=1 to 28: res$+=Mid$(L$,Random(1,27),1):next i
=res$
}
m$="METHINKS IT IS LIKE A WEASEL"
lm=len(m$)
fitness=lambda m$, lm (this$)-> {
score=0 : For i=1 to lm {score+=If(mid$(m$,i,1)=mid$(this$, i, 1)->1,0)} : =score
}
Mutate$=lambda$ l$ (w$)-> {
a=random(1,28) : insert a, 1 w$=mid$(l$, random(1,27),1)
If random(3)=1 Then b=a:while b=a {b=random(1,28)} : insert b, 1 w$=mid$(l$, random(1,27),1)
=w$
}
Mutate1$=lambda$ l$ (w$)-> {
insert random(1,28), 1 w$=mid$(l$, random(1,27),1) : =w$
}
f$=randomstring$()
topscore=0
last=0
Pen 11 {Print "Fitness |Target:", @(16),m$, @(47),"|Total Strings"}
Print Over $(3,8), str$(topscore/28,"##0.0%"),"",$(0),f$, 0
count=0
gen=30
mut=0
{
last=0
Dim a$(1 to gen)<<mutate$(f$)
mut+=gen
oldscore=topscore
For i=1 to gen {
topscore=max.data(topscore, fitness(a$(i)))
If oldscore<topscore Then last=i:Exit
}
If last>0 Then {
f$=a$(last) : gen=30 : If topscore=10 Then mutate$=mutate1$
} Else gen+=50
Print Over $(3,8), str$(topscore/28,"##0.0%"), "",$(0),f$, mut : refresh
count+=min(gen,i)
If topscore<28 Then loop
}
Print
Print "Results"
Print "I found this:"; a$(i)
Print "Total strings which evalute fitness:"; count
Print "Done"
}
WeaselAlgorithm- Output:
Fitness |Target: METHINKS IT IS LIKE A WEASEL |Total strings
3,6% ZZBZSVEOWPSQGJXNIXTFQCDQTJFE 30
7,1% ZZBZSVEOWPSQGJXNIXTFQCDQAJFE 60
14,3% ZZBZSVEOWPTQGJXNIXTFACDQAJFE 90
17,9% ZZBZSVEOWPTQGJXNIXTFA DQAJFE 200
21,4% ZEBZSVEOWPTQGJXNIXTFA DQAJFE 230
25,0% ZEBZSVEOWPTQGJXNIXT A DQAJFE 260
28,6% MEBZSVEOCPTQGJXNIXT A DQAJFE 290
32,1% MEBZSVEOCITQGJXNIXT A DQAJFE 320
35,7% MEBZSVEOCITQGJXNIKT A DQAJFE 350
39,3% MEBZSVEOCITQGJ NIKT A DQAJFE 380
42,9% MEBZSVEOCITQGJ NIKT A WQAJFE 410
46,4% MEBZSVESCITQGJ NIKT A WQAJFE 440
50,0% MEBZSVESCITQIJ NIKT A WQAJFE 680
53,6% MEBZSVESCIT IJ NIKT A WQAJFE 1100
57,1% MEBZSVESCIT IJ LIKT A WQAJFE 1130
60,7% MEBZSVKSCIT IJ LIKT A WQAJFE 1240
64,3% MEBZSVKS IT IJ LIKT A WQAJFE 1480
67,9% MEBZSNKS IT IJ LIKT A WQAJFE 1900
71,4% MEBHSNKS IT IJ LIKT A WQAJFE 2010
75,0% METHSNKS IT IJ LIKT A WQAJFE 2430
78,6% METHSNKS IT IJ LIKE A WQAJFE 2670
82,1% METHSNKS IT IJ LIKE A WQAJFL 3090
85,7% METHSNKS IT IJ LIKE A WEAJFL 3330
89,3% METHSNKS IT IJ LIKE A WEASFL 3980
92,9% METHINKS IT IJ LIKE A WEASFL 4400
96,4% METHINKS IT IJ LIKE A WEASEL 5050
100,0% METHINKS IT IS LIKE A WEASEL 5290
Results
I found this:METHINKS IT IS LIKE A WEASEL
Total strings which evaluate fitness:3230
Version 2
The second version check fitness for all strings until became 28 (100%)
Also here we have one Mutate function which change letters using 5% probability for each place in the parent string.
Module WeaselAlgorithm2 {
Print "Evolutionary Algorithm"
\\ Weasel Algorithm
\\ Using dynamic array, which expand if no fitness change,
\\ and reduce to minimum when fitness changed
l$="ABCDEFGHIJKLMNOPQRSTUVWXYZ "
randomstring$=lambda$ l$ ->{
res$=""
For i=1 to 28: res$+=Mid$(L$,Random(1,27),1):next i
=res$
}
m$="METHINKS IT IS LIKE A WEASEL"
lm=len(m$)
fitness=lambda m$, lm (this$)-> {
score=0 : For i=1 to lm {score+=If(mid$(m$,i,1)=mid$(this$, i, 1)->1,0)} : =score
}
Mutate$=lambda$ l$ (w$)-> {
for i=1 to len(w$) {
if random(1,100)<=5 then { insert i, 1 w$=mid$(l$, random(1,27),1) }
}
=w$
}
f$=randomstring$()
topscore=0
last=0
Pen 11 {Print "Fitness |Target:", @(16),m$, @(47),"|Total Strings"}
Print Over $(3,8), str$(topscore/28,"##0.0%"),"",$(0),f$, 0
count=0
gen=30
mut=0
{
last=0
Dim a$(1 to gen)<<mutate$(f$)
mut+=gen
oldscore=topscore
For i=1 to gen {
topscore=max.data(topscore, fitness(a$(i)))
If oldscore<topscore Then last=i: oldscore=topscore
}
If last>0 Then {
f$=a$(last) : gen=30
} Else gen+=50
Print Over $(3,8), str$(topscore/28,"##0.0%"), "",$(0),f$, mut : refresh
count+=min(gen,i)
If topscore<28 Then loop
}
Print
Print "Results"
Print "I found this:"; a$(last)
Print "Total strings which evalute fitness:"; count
Print "Done"
}
WeaselAlgorithm2m4
This is written in POSIX m4, but seems to run least annoyingly in GNU m4. (For instance, OpenBSD m4 may resist attempts to use the interrupt key.)
You may lose patience with it, but it is written in m4, not optimized Fortran. I have run it successfully to completion.
Note that it writes to a file called ‘__random_number__’. The random number generator here is the best I have come up with that works in all the m4 implementations I have installed. (Something using sysval did not work in OpenBSD m4.)
This is not the best use of m4, and I am far from an m4 guru, but it seemed unlikely anyone else was going to write an m4 example for this task. The better my m4 skills, the better I am going to be at everything that can be preprocessed, and at GNU Autotools (which, unlike many, I like a lot). So I wrote this.
divert(-1)
# Get a random number from 0 to one less than $1.
# (Note that this is not a very good RNG. Also it writes a file.)
#
# Usage: randnum(N) (Produces a random integer in 0..N-1)
#
define(`randnum',
`syscmd(`echo $RANDOM > __random_number__')eval(include(__random_number__) % ( $1 ))')
# The *target* specified in the Rosetta Code task.
define(`target',`METHINKS IT IS LIKE A WEASEL')
define(`alphabet',`ABCDEFGHIJKLMNOPQRSTUVWXYZ ')
define(`random_letter',`substr(alphabet,randnum(len(alphabet)),1)')
define(`create_primogenitor',`_$0(`')')
define(`_create_primogenitor',`ifelse(len(`$1'),len(target),`$1',
`$0(`$1'random_letter)')')
# The *parent* specified in the Rosetta Code task.
define(`parent',`'create_primogenitor)
#
# Usage: mutate_letter(STRING,INDEX)
#
define(`mutate_letter',
`substr(`$1',0,`$2')`'random_letter`'substr(`$1',incr(`$2'))')
#
# Usage: mutate_letter_at_rate(STRING,INDEX,MUTATION_RATE)
#
define(`mutate_letter_at_rate',
`ifelse(eval(randnum(100) < ($3)),1,`mutate_letter(`$1',`$2')',`$1')')
# The *mutate* procedure specified in the Rosetta Code task. The
# mutation rate is given in percents.
#
# Usage: mutate(STRING,MUTATION_RATE)
#
define(`mutate',`_$0(`$1',`$2',len(`$1'))')
define(`_mutate',
`ifelse($3,0,`$1',
`$0(mutate_letter_at_rate(`$1',decr($3),`$2'),`$2',decr($3))')')
# The *fitness* procedure specified in the Rosetta Code
# task. "Fitness" here is simply how many letters match.
#
# Usage: fitness(STRING)
#
define(`fitness',`_$0(`$1',target,0)')
define(`_fitness',
`ifelse(`$1',`',$3,
`ifelse(`'substr(`$1',0,1),`'substr(`$2',0,1),
`$0(`'substr(`$1',1),`'substr(`$2',1),incr($3))',
`$0(`'substr(`$1',1),`'substr(`$2',1),$3)')')')
#
# Usage: have_child(PARENT,MUTATION_RATE)
#
# The result is either the parent or the child: whichever has the
# greater fitness. If they are equally fit, one is chosen arbitrarily.
# (Note that, in the current implementation, fitnesses are not
# memoized.)
#
define(`have_child',
`pushdef(`_child_',mutate(`$1',`$2'))`'dnl
ifelse(eval(fitness(`'_child_) < fitness(`$1')),1,`$1',`_child_')`'dnl
popdef(`_child_')')
#
# Usage: next_parent(PARENT,NUM_CHILDREN,MUTATION_RATE)
#
# Note that a string is discarded as soon as it is known it will not
# be in the next generation. If some strings have the same highest
# fitness, one of them is chosen arbitrarily.
#
define(`next_parent',`_$0(`$1',`$2',`$3',`$1')')
define(`_next_parent',
`ifelse(`$2',0,`$1',
`$0(`'have_child(`$4',`$3'),decr(`$2'),`$3',`$4')')')
define(`repeat_until_equal',
`ifelse(`$1',`'target,`[$1]',
`pushdef(`_the_horta_',`'next_parent(`$1',`$2',`$3'))`'dnl
[_the_horta_]
$0(`'_the_horta_,`$2',`$3')`'dnl
popdef(`_the_horta_')')')
divert`'dnl
[parent]
repeat_until_equal(parent,10,10)Mathematica / Wolfram Language
target = "METHINKS IT IS LIKE A WEASEL";
alphabet = Append[CharacterRange["A", "Z"], " "];
fitness = HammingDistance[target, #] &;
mutate[str_String, rate_ : 0.01] := StringReplace[
str,
_ /; RandomReal[] < rate :> RandomChoice[alphabet]
]
mutationRate = 0.02; c = 100;
NestWhileList[
First@MinimalBy[
Thread[mutate[ConstantArray[#, c], mutationRate]],
fitness
] &,
mutate[target, 1],
fitness@# > 0 &
] // ListAnimate
MATLAB
This solution implements a class called EvolutionaryAlgorithm, the members of the class are the variables required by the task description. You can see them using the disp() function on an instance of the class. To use this class you only need to specify the target, mutation rate, number of children (called C in the task spec), and maximum number of evolutionary cycles. After doing so, call the evolve() function on the class instance to start the evolution cycle. Note, the fitness function computes the hamming distance between the target string and another string, this can be changed if a better heuristic exists.
To use this code, create a folder in your MATLAB directory titled "@EvolutionaryAlgorithm". Within that folder save this code in a file named "EvolutionaryAlgorithm.m".
%This class impliments a string that mutates to a target
classdef EvolutionaryAlgorithm
properties
target;
parent;
children = {};
validAlphabet;
%Constants
numChildrenPerIteration;
maxIterations;
mutationRate;
end
methods
%Class constructor
function family = EvolutionaryAlgorithm(target,mutationRate,numChildren,maxIterations)
family.validAlphabet = char([32 (65:90)]); %Space char and A-Z
family.target = target;
family.children = cell(numChildren,1);
family.numChildrenPerIteration = numChildren;
family.maxIterations = maxIterations;
family.mutationRate = mutationRate;
initialize(family);
end %class constructor
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%Helper functions and class get/set functions
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%setAlphabet() - sets the valid alphabet for the current instance
%of the EvolutionaryAlgorithm class.
function setAlphabet(family,alphabet)
if(ischar(alphabet))
family.validAlphabet = alphabet;
%Makes change permanent
assignin('caller',inputname(1),family);
else
error 'New alphabet must be a string or character array';
end
end
%setTarget() - sets the target for the current instance
%of the EvolutionaryAlgorithm class.
function setTarget(family,target)
if(ischar(target))
family.target = target;
%Makes change permanent
assignin('caller',inputname(1),family);
else
error 'New target must be a string or character array';
end
end
%setMutationRate() - sets the mutation rate for the current instance
%of the EvolutionaryAlgorithm class.
function setMutationRate(family,mutationRate)
if(isnumeric(mutationRate))
family.mutationRate = mutationRate;
%Makes change permanent
assignin('caller',inputname(1),family);
else
error 'New mutation rate must be a double precision number';
end
end
%setMaxIterations() - sets the maximum number of iterations during
%evolution for the current instance of the EvolutionaryAlgorithm class.
function setMaxIterations(family,maxIterations)
if(isnumeric(maxIterations))
family.maxIterations = maxIterations;
%Makes change permanent
assignin('caller',inputname(1),family);
else
error 'New maximum amount of iterations must be a double precision number';
end
end
%display() - overrides the built-in MATLAB display() function, to
%display the important class variables
function display(family)
disp([sprintf('Target: %s\n',family.target)...
sprintf('Parent: %s\n',family.parent)...
sprintf('Valid Alphabet: %s\n',family.validAlphabet)...
sprintf('Number of Children: %d\n',family.numChildrenPerIteration)...
sprintf('Mutation Rate [0,1]: %d\n',family.mutationRate)...
sprintf('Maximum Iterations: %d\n',family.maxIterations)]);
end
%disp() - overrides the built-in MATLAB disp() function, to
%display the important class variables
function disp(family)
display(family);
end
%randAlphabetElement() - Generates a random character from the
%valid alphabet for the current instance of the class.
function elements = randAlphabetElements(family,numChars)
%Sample the valid alphabet randomly from the uniform
%distribution
N = length(family.validAlphabet);
choices = ceil(N*rand(1,numChars));
elements = family.validAlphabet(choices);
end
%initialize() - Sets the parent to a random string of length equal
%to the length of the target
function parent = initialize(family)
family.parent = randAlphabetElements(family,length(family.target));
parent = family.parent;
%Makes changes to the instance of EvolutionaryAlgorithm permanent
assignin('caller',inputname(1),family);
end %initialize
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%Functions required by task specification
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%mutate() - generates children from the parent and mutates them
function mutate(family)
sizeParent = length(family.parent);
%Generate mutatant children sequentially
for child = (1:family.numChildrenPerIteration)
parentCopy = family.parent;
for charIndex = (1:sizeParent)
if (rand(1) < family.mutationRate)
parentCopy(charIndex) = randAlphabetElements(family,1);
end
end
family.children{child} = parentCopy;
end
%Makes changes to the instance of EvolutionaryAlgorithm permanent
assignin('caller',inputname(1),family);
end %mutate
%fitness() - Computes the Hamming distance between the target
%string and the string input as the familyMember argument
function theFitness = fitness(family,familyMember)
if not(ischar(familyMember))
error 'The second argument must be a string';
end
theFitness = sum(family.target == familyMember);
end
%evolve() - evolves the family until the target is reached or it
%exceeds the maximum amount of iterations
function [iteration,mostFitFitness] = evolve(family)
iteration = 0;
mostFitFitness = 0;
targetFitness = fitness(family,family.target);
disp(['Target fitness is ' num2str(targetFitness)]);
while (mostFitFitness < targetFitness) && (iteration < family.maxIterations)
iteration = iteration + 1;
mutate(family);
parentFitness = fitness(family,family.parent);
mostFit = family.parent;
mostFitFitness = parentFitness;
for child = (1:family.numChildrenPerIteration)
childFitness = fitness(family,family.children{child});
if childFitness > mostFitFitness
mostFit = family.children{child};
mostFitFitness = childFitness;
end
end
family.parent = mostFit;
disp([num2str(iteration) ': ' mostFit ' - Fitness: ' num2str(mostFitFitness)]);
end
%Makes changes to the instance of EvolutionaryAlgorithm permanent
assignin('caller',inputname(1),family);
end %evolve
end %methods
end %classdef
Sample Output: (Some evolutionary cycles omitted for brevity)
>> instance = EvolutionaryAlgorithm('METHINKS IT IS LIKE A WEASEL',.08,50,1000)
Target: METHINKS IT IS LIKE A WEASEL
Parent: UVEOCXXFBGDCSFNMJQNWTPJ PCVA
Valid Alphabet: ABCDEFGHIJKLMNOPQRSTUVWXYZ
Number of Children: 50
Mutation Rate [0,1]: 8.000000e-002
Maximum Iterations: 1000
>> evolve(instance);
Target fitness is 28
1: MVEOCXXFBYD SFCMJQNWTPM PCVA - Fitness: 2
2: MEEOCXXFBYD SFCMJQNWTPM PCVA - Fitness: 3
3: MEEHCXXFBYD SFCMJXNWTPM ECVA - Fitness: 4
4: MEEHCXXFBYD SFCMJXNWTPM ECVA - Fitness: 4
5: METHCXAFBYD SFCMJXNWXPMARPVA - Fitness: 5
6: METHCXAFBYDFSFCMJXNWX MARSVA - Fitness: 6
7: METHCXKFBYDFBFCQJXNWX MATSVA - Fitness: 7
8: METHCXKFBYDFBF QJXNWX MATSVA - Fitness: 8
9: METHCXKFBYDFBF QJXNWX MATSVA - Fitness: 8
10: METHCXKFUYDFBF QJXNWX MITSEA - Fitness: 9
20: METHIXKF YTBOF LIKN G MIOSEI - Fitness: 16
30: METHIXKS YTCOF LIKN A MIOSEL - Fitness: 19
40: METHIXKS YTCIF LIKN A MEUSEL - Fitness: 21
50: METHIXKS YT IS LIKE A PEUSEL - Fitness: 24
100: METHIXKS YT IS LIKE A WEASEL - Fitness: 26
150: METHINKS YT IS LIKE A WEASEL - Fitness: 27
195: METHINKS IT IS LIKE A WEASEL - Fitness: 28
Genetic Algorithm Example
This solution uses a subset of evolutionary programming called the Genetic Algorithm. It is very similar to the basic evolutionary algorithm, but instead of just using mutations it also makes use of other genetic operators. The algorithm begins by importing the target text (in this case 'METHINKS IT IS LIKE A WEASEL') and then the algorithm performs genetic operations until the target string is obtained or the maximum number of iterations is reached (which will never happen with the given target string). The algorithm first measures how fit each potential answer is, and then selects strings to perform operations on. The selected answers go through the crossover stage where their data is split and recombined into new potential answers. Then a chance for the answer to mutate slightly occurs and the algorithm repeats itself.
Presented is very efficient and vectorized version of the genetic algorithm. To run the algorithm simply copy and paste the code into a script and hit run. You can adjust the style of selection and crossover used to learn more about how they effect solutions. The algorithm can also handle any target string that uses ASCII characters and will allow for any phrase to be used regardless of length.
%% Genetic Algorithm -- Solves For A User Input String
% #### PLEASE NOTE: you can change the selection and crossover type in the
% parameters and see how the algorithm changes. ####
clear;close all;clc; %Clears variables, closes windows, and clears the command window
tic % Begins the timer
%% Select Target String
target = 'METHINKS IT IS LIKE A WEASEL';
% *Can Be Any String With Any Values and Any Length!*
% but for this example we use 'METHINKS IT IS LIKE A WEASEL'
%% Parameters
popSize = 1000; % Population Size (100-10000 generally produce good results)
genome = length(target); % Genome Size
mutRate = .01; % Mutation Rate (5%-25% produce good results)
S = 4; % Tournament Size (2-6 produce good results)
best = Inf; % Initialize Best (arbitrarily large)
MaxVal = max(double(target)); % Max Integer Value Needed
ideal = double(target); % Convert Target to Integers
selection = 0; % 0: Tournament
% 1: 50% Truncation
crossover = 1; % 0: Uniform crossover
% 1: 1 point crossover
% 2: 2 point crossover
%% Initialize Population
Pop = round(rand(popSize,genome)*(MaxVal-1)+1); % Creates Population With Corrected Genome Length
for Gen = 1:1e6 % A Very Large Number Was Chosen, But Shouldn't Be Needed
%% Fitness
% The fitness function starts by converting the characters into integers and then
% subtracting each element of each member of the population from each element of
% the target string. The function then takes the absolute value of
% the differences and sums each row and stores the function as a mx1 matrix.
F = sum(abs(bsxfun(@minus,Pop,ideal)),2);
% Finding Best Members for Score Keeping and Printing Reasons
[current,currentGenome] = min(F); % current is the minimum value of the fitness array F
% currentGenome is the index of that value in the F array
% Stores New Best Values and Prints New Best Scores
if current < best
best = current;
bestGenome = Pop(currentGenome,:); % Uses that index to find best value
fprintf('Gen: %d | Fitness: %d | ',Gen, best); % Formatted printing of generation and fitness
disp(char(bestGenome)); % Best genome so far
elseif best == 0
break % Stops program when we are done
end
%% Selection
% TOURNAMENT
if selection == 0
T = round(rand(2*popSize,S)*(popSize-1)+1); % Tournaments
[~,idx] = min(F(T),[],2); % Index to Determine Winners
W = T(sub2ind(size(T),(1:2*popSize)',idx)); % Winners
% 50% TRUNCATION
elseif selection == 1
[~,V] = sort(F,'descend'); % Sort Fitness in Ascending Order
V = V(popSize/2+1:end); % Winner Pool
W = V(round(rand(2*popSize,1)*(popSize/2-1)+1))'; % Winners
end
%% Crossover
% UNIFORM CROSSOVER
if crossover == 0
idx = logical(round(rand(size(Pop)))); % Index of Genome from Winner 2
Pop2 = Pop(W(1:2:end),:); % Set Pop2 = Pop Winners 1
P2A = Pop(W(2:2:end),:); % Assemble Pop2 Winners 2
Pop2(idx) = P2A(idx); % Combine Winners 1 and 2
% 1-POINT CROSSOVER
elseif crossover == 1
Pop2 = Pop(W(1:2:end),:); % New Population From Pop 1 Winners
P2A = Pop(W(2:2:end),:); % Assemble the New Population
Ref = ones(popSize,1)*(1:genome); % The Reference Matrix
idx = (round(rand(popSize,1)*(genome-1)+1)*ones(1,genome))>Ref; % Logical Indexing
Pop2(idx) = P2A(idx); % Recombine Both Parts of Winners
% 2-POINT CROSSOVER
elseif crossover == 2
Pop2 = Pop(W(1:2:end),:); % New Pop is Winners of old Pop
P2A = Pop(W(2:2:end),:); % Assemble Pop2 Winners 2
Ref = ones(popSize,1)*(1:genome); % Ones Matrix
CP = sort(round(rand(popSize,2)*(genome-1)+1),2); % Crossover Points
idx = CP(:,1)*ones(1,genome)<Ref&CP(:,2)*ones(1,genome)>Ref; % Index
Pop2(idx)=P2A(idx); % Recombine Winners
end
%% Mutation
idx = rand(size(Pop2))<mutRate; % Index of Mutations
Pop2(idx) = round(rand([1,sum(sum(idx))])*(MaxVal-1)+1); % Mutated Value
%% Reset Poplulations
Pop = Pop2;
end
toc % Ends timer and prints elapsed time
Sample Output: (The Algorithm was run with 1000 population members, Tournament Selection (with tournament size of 4), 1-Point Crossover, and a mutation rate of 10%).
Gen: 1 | Fitness: 465 | C�I1%G+<%?R�8>9�JU#(E�UO�PHI
Gen: 2 | Fitness: 429 | W=P6>D�I)VU6$T 99,� B�BMP0JH
Gen: 3 | Fitness: 366 | P�;R08AS�GJ�IS&T38IE�)SJERLJ
Gen: 4 | Fitness: 322 | KI8M5LAS�GJ�IS�SP�@)D�V@
JCP
Gen: 5 | Fitness: 295 | UAUR08AS�GJ�IS�8HG*�+�=C?UB(
Gen: 6 | Fitness: 259 | VCUQH35S�HR4.L�ISJQ%J�OC*T=E
Gen: 7 | Fitness: 226 | LFB8GPET(LODKQ�KQ<K E*PEMA6I
Gen: 8 | Fitness: 192 | EPKOLCIR�QQ�NF�QG:B(D/U>BQGF
Gen: 9 | Fitness: 159 | N8R7?SOU�NO$OK O?K?!;�MB?QHG
Gen: 10 | Fitness: 146 | TGN@EQR4)PS%IS#TFJQ%A!U>BVLI
Gen: 11 | Fitness: 120 | L?VMALJS%?R EK IILE�6'RRERLJ
Gen: 12 | Fitness: 102 | R@T9COMR�NU CS*R?K?!; VD>LCL
Gen: 13 | Fitness: 96 | NENMVOMR�NU CS*R?K?!; VD>LCL
Gen: 14 | Fitness: 82 | REJGNPMU�KR CS JKI@+D�UD?QHG
Gen: 15 | Fitness: 75 | NETI=HPQ�FT ID EFKE D"WD>QDQ
Gen: 16 | Fitness: 70 | R@TKCOOT)@R$IS KKLE�D"WC?UBJ
Gen: 17 | Fitness: 61 | NESIKQRP�NU CS�MFKE ; SEETCP
Gen: 18 | Fitness: 57 | LFSGLPTN�NU GQ IIKE D"VD>LCL
Gen: 19 | Fitness: 40 | NENKJLMS�GS%IS#MFKE B UFATCL
Gen: 21 | Fitness: 39 | NETIGPEU�KR IS IIKD"? UFDQEK
Gen: 22 | Fitness: 33 | NETGCOMT�LU IS#MFKE B UFATCL
Gen: 23 | Fitness: 32 | NETIKNPQ�NU IS#IIKE B UFATCL
Gen: 24 | Fitness: 27 | NETKJLMS�LU IS MFKE B UFATCL
Gen: 25 | Fitness: 23 | LETIKOMS LU IS IIKE D WEDQEK
Gen: 26 | Fitness: 22 | NETIKMJS LU IS IIKE D WEDQEK
Gen: 27 | Fitness: 20 | LETIKOMS LU IS KILE B"WFATCL
Gen: 28 | Fitness: 19 | NESGJQJS�GU IS KIKE B WFATEK
Gen: 29 | Fitness: 16 | NETIHPMS KR IS KIKE B WFATEK
Gen: 30 | Fitness: 15 | NESHLPKS KU IS KIKE B WFATEK
Gen: 31 | Fitness: 13 | NETGGNKS KU IS KIKE C WFATEK
Gen: 32 | Fitness: 12 | NETHGNJS IU IS JIKE B WFATCL
Gen: 33 | Fitness: 11 | NETIJPKS IU IS KIKE B WFATEK
Gen: 35 | Fitness: 8 | LEUIHNJS IT IS JIKE A WEATEL
Gen: 37 | Fitness: 7 | NETIHNJS IS IS LIKE B WFASEL
Gen: 38 | Fitness: 6 | NETHGNJS IT IS LIKE A WFASEK
Gen: 39 | Fitness: 4 | METGHNKS IT IS LIKE B WEATEL
Gen: 42 | Fitness: 3 | NETHINKS IT IS KIKE B WEASEL
Gen: 43 | Fitness: 2 | NETHINKS IT IS LIKE A WFASEL
Gen: 44 | Fitness: 1 | METHHNKS IT IS LIKE A WEASEL
Gen: 46 | Fitness: 0 | METHINKS IT IS LIKE A WEASEL
Elapsed time is 0.099618 seconds.
Nanoquery
import Nanoquery.Util
target = "METHINKS IT IS LIKE A WEASEL"
tbl = "ABCDEFGHIJKLMNOPQRSTUVWXYZ "
copies = 30
chance = 0.09
rand = new(Random)
// returns a random string of the specified length
def rand_string(length)
global tbl
global rand
ret = ""
for i in range(1, length)
ret += tbl[rand.getInt(len(tbl))]
end
return ret
end
// gets the fitness of a given string
def fitness(string)
global target
fit = 0
for i in range(0, len(string) - 1)
if string[i] != target[i]
fit -= 1
end
end
return fit
end
// mutates the specified string with a chance of 0.09 by default
def mutate(string)
global chance
global rand
global tbl
mutated = string
for i in range(0, len(mutated) - 1)
if rand.getFloat() <= chance
mutated[i] = tbl[rand.getInt(len(tbl))]
end
end
return mutated
end
// a function to find the index of the string with the best fitness
def most_fit(strlist)
global target
best_score = -(len(target) + 1)
best_index = 0
for j in range(0, len(strlist) - 1)
fit = fitness(strlist[j])
if fit > best_score
best_index = j
best_score = fit
end
end
return {best_index, best_score}
end
parent = rand_string(len(target)); iter = 1
while parent != target
children = {}
for i in range(1, 30)
children.append(mutate(parent))
end
fit = most_fit(children)
parent = children[fit[0]]
print format("iter %d, score %d: %s\n", iter, fit[1], parent)
iter += 1
end- Output:
iter 1, score -26: QVAZCNKALPVKLYYNCECRURIRLASN iter 2, score -25: QVAZ NKALPVKLYYNIECRURIRLASN iter 3, score -24: QVAZINKALPVKLYYNIRCRURIRLASN iter 4, score -24: QVAZINKAYPVKLYYNIRCRURISLASN iter 5, score -23: QVAZINKAYPTKLYYNIRCPURISLASN iter 6, score -21: QAAHINKAYPTKLYYNIRCPARISLASN iter 7, score -20: QATHINKAZPTKLHYNIWCPARISLASN iter 8, score -19: QETHINKAZKTKEHYVIWIPARISLASB iter 9, score -19: PETHINKAZKTKUHYVIWIPARISLASB iter 10, score -18: PETHINKSZKTKUHYVIWIPARIBIASB ... iter 202, score -1: METHINKS IT IS LIKE A WEESEL iter 203, score -1: METHINKS IT IS LIKE A WEESEL iter 204, score -1: METHINKS IT IS LIKE A WEESEL iter 205, score -1: METHINKS IT IS LIKE A WEESEL iter 206, score -1: METHINKS IT IS LIKE A WEESEL iter 207, score 0: METHINKS IT IS LIKE A WEASEL
Nim
import random
const
Target = "METHINKS IT IS LIKE A WEASEL"
Alphabet = " ABCDEFGHIJLKLMNOPQRSTUVWXYZ"
P = 0.05
C = 100
proc negFitness(trial: string): int =
for i in 0 .. trial.high:
if Target[i] != trial[i]:
inc result
proc mutate(parent: string): string =
for c in parent:
result.add (if rand(1.0) < P: sample(Alphabet) else: c)
randomize()
var parent = ""
for _ in 1..Target.len:
parent.add sample(Alphabet)
var i = 0
while parent != Target:
var copies = newSeq[string](C)
for i in 0 .. copies.high:
copies[i] = parent.mutate()
var best = copies[0]
for i in 1 .. copies.high:
if negFitness(copies[i]) < negFitness(best):
best = copies[i]
parent = best
echo i, " ", parent
inc i
- Output:
0 DDTAXEPAFNI RIKNLUBKPXKBFHGA 1 DDTJXEPAFNI RIKNLUB PXKBFHGA 2 CDTJXEPAFNI RI NLUB ZXKBFHGA 3 CDTJXEPAFNI RI KLUB ZXKEFHGA [...] 37 METJINKS IT IS LIBE A WEANEL [...] 70 MET INKS IT IS LIKE A WEASEL 71 METHINKS IT IS LIKE A WEASEL
Objeck
bundle Default {
class Evolutionary {
target : static : String;
possibilities : static : Char[];
C : static : Int;
minMutateRate : static : Float;
perfectFitness : static : Int;
parent : static : String ;
rand : static : Float;
function : Init() ~ Nil {
target := "METHINKS IT IS LIKE A WEASEL";
possibilities := "ABCDEFGHIJKLMNOPQRSTUVWXYZ "->ToCharArray();
C := 100;
minMutateRate := 0.09;
perfectFitness := target->Size();
}
function : fitness(trial : String) ~ Int {
retVal := 0;
each(i : trial) {
if(trial->Get(i) = target->Get(i)) {
retVal += 1;
};
};
return retVal;
}
function : newMutateRate() ~ Float {
x : Float := perfectFitness - fitness(parent);
y : Float := perfectFitness->As(Float) * (1.01 - minMutateRate);
return x / y;
}
function : mutate(parent : String, rate : Float) ~ String {
retVal := "";
each(i : parent) {
rand := Float->Random();
if(rand <= rate) {
rand *= 1000.0;
intRand := rand->As(Int);
index : Int := intRand % possibilities->Size();
retVal->Append(possibilities[index]);
}
else {
retVal->Append(parent->Get(i));
};
};
return retVal;
}
function : Main(args : String[]) ~ Nil {
Init();
parent := mutate(target, 1.0);
iter := 0;
while(target->Equals(parent) <> true) {
rate := newMutateRate();
iter += 1;
if(iter % 100 = 0){
IO.Console->Instance()->Print(iter)->Print(": ")->PrintLine(parent);
};
bestSpawn : String;
bestFit := 0;
for(i := 0; i < C; i += 1;) {
spawn := mutate(parent, rate);
fitness := fitness(spawn);
if(fitness > bestFit) {
bestSpawn := spawn;
bestFit := fitness;
};
};
if(bestFit > fitness(parent)) {
parent := bestSpawn;
};
};
parent->PrintLine();
}
}
}
}Output:
100: DETHILBMDEB QR YIEGYEBWCCSBN 200: D THIWTXEXH IO SVUDHEEWQASEL 300: DVTHINTILS RIO SVGEKNEWEASEU 400: MFTH AWBLIXNIE STFE AWWEASEJ 500: MFTHIAWDIIRMIY QTFE AWWEASEJ 600: MZTCIAKDQIRNIY NWFE A WEASEJ 700: MZTCIAKDQIRNIY NWFE A WEASEJ 800: MZTCIAKDQIRNIY NWFE A WEASEJ 900: MZTCIAKOWIRNIY NILE A WEASEJ 1000: MZTCIAKOWIRNIY NILE A WEASEJ 1100: MZTCIAKOWIRNIY NILE A WEASEJ 1200: MZTCIAKOWIRNIY NILE A WEASEJ 1300: METCITKSTIRSIY JYKE A WDASEJ 1400: METHITKSTIJ IB FYKE A WDASEJ 1500: METHINKSZIJ IB FYKE A WEASEQ METHINKS IT IS LIKE A WEASEL
OCaml
let target = "METHINKS IT IS LIKE A WEASEL"
let charset = "ABCDEFGHIJKLMNOPQRSTUVWXYZ "
let tlen = String.length target
let clen = String.length charset
let () = Random.self_init()
let parent =
let s = String.create tlen in
for i = 0 to tlen-1 do
s.[i] <- charset.[Random.int clen]
done;
s
let fitness ~trial =
let rec aux i d =
if i >= tlen then d else
aux (i+1) (if target.[i] = trial.[i] then d+1 else d) in
aux 0 0
let mutate parent rate =
let s = String.copy parent in
for i = 0 to tlen-1 do
if Random.float 1.0 > rate then
s.[i] <- charset.[Random.int clen]
done;
s, fitness s
let () =
let i = ref 0 in
while parent <> target do
let pfit = fitness parent in
let rate = float pfit /. float tlen in
let tries = Array.init 200 (fun _ -> mutate parent rate) in
let min_by (a, fa) (b, fb) = if fa > fb then a, fa else b, fb in
let best, f = Array.fold_left min_by (parent, pfit) tries in
if !i mod 100 = 0 then
Printf.printf "%5d - '%s' (fitness:%2d)\n%!" !i best f;
String.blit best 0 parent 0 tlen;
incr i
done;
Printf.printf "%5d - '%s'\n" !i parent
Octave
global target;
target = split("METHINKS IT IS LIKE A WEASEL", "");
charset = ["A":"Z", " "];
p = ones(length(charset), 1) ./ length(charset);
parent = discrete_rnd(charset, p, length(target), 1);
mutaterate = 0.1;
C = 1000;
function r = fitness(parent, target)
r = sum(parent == target) ./ length(target);
endfunction
function r = mutate(parent, mutaterate, charset)
r = parent;
p = unifrnd(0, 1, length(parent), 1);
nmutants = sum( p < mutaterate );
if (nmutants)
s = discrete_rnd(charset, ones(length(charset), 1) ./ length(charset),nmutants,1);
r( p < mutaterate ) = s;
endif
endfunction
function r = evolve(parent, mutatefunc, fitnessfunc, C, mutaterate, charset)
global target;
children = [];
for i = 1:C
children = [children, mutatefunc(parent, mutaterate, charset)];
endfor
children = [parent, children];
fitval = [];
for i = 1:columns(children)
fitval = [fitval, fitnessfunc(children(:,i), target)];
endfor
[m, im] = max(fitval);
r = children(:, im);
endfunction
function printgen(p, t, i)
printf("%3d %5.2f %s\n", i, fitness(p, t), p');
endfunction
i = 0;
while( !all(parent == target) )
i++;
parent = evolve(parent, @mutate, @fitness, C, mutaterate, charset);
if ( mod(i, 1) == 0 )
printgen(parent, target, i);
endif
endwhile
disp(parent');
Oforth
200 Constant new: C
5 Constant new: RATE
: randChar // -- c
27 rand dup 27 == ifTrue: [ drop ' ' ] else: [ 'A' + 1- ] ;
: fitness(a b -- n)
a b zipWith(#==) sum ;
: mutate(s -- s')
s map(#[ 100 rand RATE <= ifTrue: [ drop randChar ] ]) charsAsString ;
: evolve(target)
| parent |
ListBuffer init(target size, #randChar) charsAsString ->parent
1 while ( parent target <> ) [
ListBuffer init(C, #[ parent mutate ]) dup add(parent)
maxFor(#[ target fitness ]) dup ->parent . dup println 1+
] drop ;- Output:
>evolve("METHINKS IT IS LIKE A WEASEL")
WHQHNXXAWACZKTTIHKVBCYLPATN 1
WHQHNXXAWACZKTTIHKV CYLPATN 2
WHQHNXXAWACZKTTIHKV C LPATC 3
WHQHNXXSWATZKTTIHKV C LPATC 4
WHQHNXXSWATCKTTIHKV C LEATC 5
WHQHNXXSWATCKTTIHKV C LEATCL 6
WHQHNXXSWATCKFTIHKV C LEASCL 7
WHQHNXXSWATCKF IHKV C LEASCL 8
WHQHNXXSWATZKF IHKV A LEASCL 9
MHQHNXXSWATZKF IHKV A LEASCL 10
MATHNXXSWATZKF ICKV A LEASCL 11
MATHIXXSBATZKF ICKV A LEASCL 12
MATHIXXSBATZKS ICKV A LEASCL 13
MATHIXXSBATZKS BCKV A LEASCL 14
MATHIXXSBATZKS LCKV A LEASCL 15
MATHIXXS ATZKS LSKV A LEASCL 16
MATHIXXS ATJKS LSKV A LEASEL 17
METHIXXS ATJKS LSKV A LEASEL 18
METHIXXS ATJKS LSKE A LEASEL 19
METHINXS ATJKS LSKE A LEASEL 20
METHINXS ATJKS LSKE A WEASEL 21
METHINKS ATJKS LSKE A WEASEL 22
METHINKS ATJUS LSKE A WEASEL 23
METHINKS ATJUS LSKE A WEASEL 24
METHINKS ATJIS LSKE A WEASEL 25
METHINKS ATJIS LSKE A WEASEL 26
METHINKS ATJIS LIKE A WEASEL 27
METHINKS ATJIS LIKE A WEASEL 28
METHINKS STJIS LIKE A WEASEL 29
METHINKS STJIS LIKE A WEASEL 30
METHINKS OT IS LIKE A WEASEL 31
METHINKS OT IS LIKE A WEASEL 32
METHINKS OT IS LIKE A WEASEL 33
METHINKS OT IS LIKE A WEASEL 34
METHINKS OT IS LIKE A WEASEL 35
METHINKS IT IS LIKE A WEASEL 36
ok
OoRexx
Run with Open Object Rexx 4.1.0 by IBM Corporation 1995,2004 Rexx LA 2005-2010. Host OS: Microsoft Windows 7.
/* Weasel.rex - Me thinks thou art a weasel. - G,M.D. - 2/25/2011 */
arg C M
/* C is the number of children parent produces each generation. */
/* M is the mutation rate of each gene (character) */
call initialize
generation = 0
do until parent = target
most_fitness = fitness(parent)
most_fit = parent
do C
child = mutate(parent, M)
child_fitness = fitness(child)
if child_fitness > most_fitness then
do
most_fitness = child_fitness
most_fit = child
say "Generation" generation": most fit='"most_fit"', fitness="left(most_fitness,4)
end
end
parent = most_fit
generation = generation + 1
end
exit
initialize:
target = "METHINKS IT IS LIKE A WEASEL"
alphabet = "ABCDEFGHIJKLMNOPQRSTUVWXYZ "
c_length_target = length(target)
parent = mutate(copies(" ", c_length_target), 1.0)
do i = 1 to c_length_target
target_ch.i = substr(target,i,1)
end
return
fitness: procedure expose target_ch. c_length_target
arg parm_string
fitness = 0
do i_target = 1 to c_length_target
if substr(parm_string,i_target,1) = target_ch.i_target then
fitness = fitness + 1
end
return fitness
mutate:procedure expose alphabet
arg string, parm_mutation_rate
result = ""
do istr = 1 to length(string)
if random(1,1000)/1000 <= parm_mutation_rate then
result = result || substr(alphabet,random(1,length(alphabet)),1)
else
result = result || substr(string,istr,1)
end
return result
Output:
C:\usr\rex>weasel 10 .01 Generation 20, most fit='BZTACOQCQ CTMPIXPXBVKRUCLY F', fitness=1 Generation 30, most fit='BZTHCOQCQ CTMPIXPXBVKRUCLY F', fitness=2 Generation 34, most fit='BZTHCOQSQ CTMPIXPXBVKRUCLY F', fitness=3 Generation 61, most fit='BZTHCOQSQ CTIPIXPXBVKRUCLY F', fitness=4 Generation 95, most fit='BZTHCNQSQ CTIPIXPXBVKRUCLY F', fitness=5 Generation 107, most fit='BZTHCNQSQ CTISIXPXBVKRUCLY F', fitness=6 Generation 121, most fit='BZTHCNQS CTISIXPXBVKRUCLY F', fitness=7 Generation 129, most fit='BZTHCNQS CTISIXPXBVKRUELY F', fitness=8 Generation 142, most fit='BZTHCNQS CTISIXPXBVKRUELS F', fitness=9 Generation 143, most fit='BZTHCNQS ICTISIXPXBVKRUEHS F', fitness=10 Generation 147, most fit='BZTHCNQS ICTISIXPXBVKRUEHS L', fitness=11 Generation 154, most fit='BZTHCNQS IC ISIXPXBVKRUEHS L', fitness=12 Generation 201, most fit='BZTHCNQS IT ISIXPXBVKRUEHS L', fitness=13 Generation 213, most fit='BZTHCNQS IT ISIXPXEVKRUEHS L', fitness=14 Generation 250, most fit='BZTHCNKS IT ISIXPXEVKRUEHS L', fitness=15 Generation 268, most fit='BZTHCNKS IT ISIXPXEVKFUEAS L', fitness=16 Generation 274, most fit='BZTHCNKS IT ISIXPKEVKFUEAS L', fitness=17 Generation 292, most fit='BZTHCNKS IT ISIXPKEVKFWEAS L', fitness=18 Generation 353, most fit='BZTHCNKS IT ISIXPKEVKFWEASEL', fitness=19 Generation 358, most fit='BZTHCNKS IT ISIXPKEVK WEASEL', fitness=20 Generation 374, most fit='BETHCNKS IT ISIXPKEVK WEASEL', fitness=21 Generation 404, most fit='BETHCNKS IT ISILPKEVK WEASEL', fitness=22 Generation 405, most fit='BETHCNKS IT ISILPKE K WEASEL', fitness=23 Generation 448, most fit='FETHCNKS IT ISILPKE A WEASEL', fitness=24 Generation 679, most fit='FETHINKS IT ISILPKE A WEASEL', fitness=25 Generation 964, most fit='METHINKS IT ISILPKE A WEASEL', fitness=26 Generation 1018, most fit='METHINKS IT ISILIKE A WEASEL', fitness=27 Generation 1250, most fit='METHINKS IT IS LIKE A WEASEL', fitness=28 C:\usr\rex>
OxygenBasic
The algorithm pared down to the essentials. It takes around 1200 to 6000 mutations to attain the target. Fitness is measured by the number of beneficial mutations. The cycle ends when this is equal to the string length.
'EVOLUTION
target="METHINKS IT IS LIKE A WEASEL"
le=len target
progeny=string le,"X"
quad seed
declare QueryPerformanceCounter lib "kernel32.dll" (quad*q)
QueryPerformanceCounter seed
Function Rand(sys max) as sys
mov eax,max
inc eax
imul edx,seed,0x8088405
inc edx
mov seed,edx
mul edx
return edx
End Function
sys ls=le-1,cp=0,ct=0,ch=0,fit=0,gens=0
do '1 mutation per generation
i=1+rand ls 'mutation position
ch=64+rand 26 'mutation ascii code
if ch=64 then ch=32 'change '@' to ' '
ct=asc target,i 'target ascii code
cp=asc progeny,i 'parent ascii code
'
if ch=ct then
if cp<>ct then
mid progeny,i,chr ch 'carry improvement
fit++ 'increment fitness
end if
end if
gens++
if fit=le then exit do 'matches target
end do
print progeny " " gens 'RESULT (range 1200-6000 generations)Oz
declare
Target = "METHINKS IT IS LIKE A WEASEL"
C = 100
MutateRate = 5 %% percent
proc {Main}
X0 = {MakeN {Length Target} RandomChar}
in
for Xi in {Iterate Evolve X0} break:Break do
{System.showInfo Xi}
if Xi == Target then {Break} end
end
end
fun {Evolve Xi}
Copies = {MakeN C fun {$} {Mutate Xi} end}
in
{FoldL Copies MaxByFitness Xi}
end
fun {Mutate Xs}
{Map Xs
fun {$ X}
if {OS.rand} mod 100 < MutateRate then {RandomChar}
else X
end
end}
end
fun {MaxByFitness A B}
if {Fitness B} > {Fitness A} then B else A end
end
fun {Fitness Candidate}
{Length {Filter {List.zip Candidate Target Value.'=='} Id}}
end
Alphabet = & |{List.number &A &Z 1}
fun {RandomChar}
I = {OS.rand} mod {Length Alphabet} + 1
in
{Nth Alphabet I}
end
%% General purpose helpers
fun {Id X} X end
fun {MakeN N F}
Xs = {List.make N}
in
{ForAll Xs F}
Xs
end
fun lazy {Iterate F X}
X|{Iterate F {F X}}
end
in
{Main}PARI/GP
The algorithm given here is more general than the one described, in which letters can be inserted or deleted as well as mutated. The rate for insertions and deletions are set to 0, however, so the results are the same.
This code is inefficient (tens of milliseconds) since it converts back and forth between string and vector format. A more efficient version would keep the information in a Vecsmall instead.
target="METHINKS IT IS LIKE A WEASEL";
fitness(s)=-dist(Vec(s),Vec(target));
dist(u,v)=sum(i=1,min(#u,#v),u[i]!=v[i])+abs(#u-#v);
letter()=my(r=random(27)); if(r==26, " ", Strchr(r+65));
insert(v,x=letter())=
{
my(r=random(#v+1));
if(r==0, return(concat([x],v)));
if(r==#v, return(concat(v,[x])));
concat(concat(v[1..r],[x]),v[r+1..#v]);
}
delete(v)=
{
if(#v<2, return([]));
my(r=random(#v)+1);
if(r==1, return(v[2..#v]));
if(r==#v, return(v[1..#v-1]));
concat(v[1..r-1],v[r+1..#v]);
}
mutate(s,rateM,rateI,rateD)=
{
my(v=Vec(s));
if(random(1.)<rateI, v=insert(v));
if(random(1.)<rateD, v=delete(v));
for(i=1,#v,
if(random(1.)<rateM, v[i]=letter())
);
concat(v);
}
evolve(C,rate)=
{
my(parent=concat(vector(#target,i,letter())),ct=0);
while(parent != target,
print(parent" "fitness(parent));
my(v=vector(C,i,mutate(parent,rate,0,0)),best,t);
best=fitness(parent=v[1]);
for(i=2,C,
t=fitness(v[i]);
if(t>best, best=t; parent=v[i])
);
ct++
);
print(parent" "fitness(parent));
ct;
}
evolve(35,.05)Pascal
This Pascal version of the program displays the initial random string and every hundredth generation after that. It also displays the final generation count. Mutation happens relatively slowly, about once in every 1000 characters, but this can be changed by altering the RATE constant. Lower values for RATE actually speed up the mutations.
PROGRAM EVOLUTION (OUTPUT);
CONST
TARGET = 'METHINKS IT IS LIKE A WEASEL';
COPIES = 100; (* 100 children in each generation. *)
RATE = 1000; (* About one character in 1000 will be a mutation. *)
TYPE
STRLIST = ARRAY [1..COPIES] OF STRING;
FUNCTION RANDCHAR : CHAR;
(* Generate a random letter or space. *)
VAR RANDNUM : INTEGER;
BEGIN
RANDNUM := RANDOM(27);
IF RANDNUM = 26 THEN
RANDCHAR := ' '
ELSE
RANDCHAR := CHR(RANDNUM + ORD('A'))
END;
FUNCTION RANDSTR (SIZE : INTEGER) : STRING;
(* Generate a random string. *)
VAR
N : INTEGER;
S : STRING;
BEGIN
S := '';
FOR N := 1 TO SIZE DO
INSERT(RANDCHAR, S, 1);
RANDSTR := S
END;
FUNCTION FITNESS (CANDIDATE, GOAL : STRING) : INTEGER;
(* Count the number of correct letters in the correct places *)
VAR N, MATCHES : INTEGER;
BEGIN
MATCHES := 0;
FOR N := 1 TO LENGTH(GOAL) DO
IF CANDIDATE[N] = GOAL[N] THEN
MATCHES := MATCHES + 1;
FITNESS := MATCHES
END;
FUNCTION MUTATE (RATE : INTEGER; S : STRING) : STRING;
(* Randomly alter a string. Characters change with probability 1/RATE. *)
VAR
N : INTEGER;
CHANGE : BOOLEAN;
BEGIN
FOR N := 1 TO LENGTH(TARGET) DO
BEGIN
CHANGE := RANDOM(RATE) = 0;
IF CHANGE THEN
S[N] := RANDCHAR
END;
MUTATE := S
END;
PROCEDURE REPRODUCE (RATE : INTEGER; PARENT : STRING; VAR CHILDREN : STRLIST);
(* Generate children with random mutations. *)
VAR N : INTEGER;
BEGIN
FOR N := 1 TO COPIES DO
CHILDREN[N] := MUTATE(RATE, PARENT)
END;
FUNCTION FITTEST(CHILDREN : STRLIST; GOAL : STRING) : STRING;
(* Measure the fitness of each child and return the fittest. *)
(* If multiple children equally match the target, then return the first. *)
VAR
MATCHES, MOST_MATCHES, BEST_INDEX, N : INTEGER;
BEGIN
MOST_MATCHES := 0;
BEST_INDEX := 1;
FOR N := 1 TO COPIES DO
BEGIN
MATCHES := FITNESS(CHILDREN[N], GOAL);
IF MATCHES > MOST_MATCHES THEN
BEGIN
MOST_MATCHES := MATCHES;
BEST_INDEX := N
END
END;
FITTEST := CHILDREN[BEST_INDEX]
END;
VAR
PARENT, BEST_CHILD : STRING;
CHILDREN : STRLIST;
GENERATIONS : INTEGER;
BEGIN
RANDOMIZE;
GENERATIONS := 0;
PARENT := RANDSTR(LENGTH(TARGET));
WHILE NOT (PARENT = TARGET) DO
BEGIN
IF (GENERATIONS MOD 100) = 0 THEN WRITELN(PARENT);
GENERATIONS := GENERATIONS + 1;
REPRODUCE(RATE, PARENT, CHILDREN);
BEST_CHILD := FITTEST(CHILDREN, TARGET);
IF FITNESS(PARENT, TARGET) < FITNESS(BEST_CHILD, TARGET) THEN
PARENT := BEST_CHILD
END;
WRITE('The string was matched in ');
WRITELN(GENERATIONS, ' generations.')
END.
Perl
This implementation usually converges in less than 70 iterations.
use List::Util 'reduce';
use List::MoreUtils 'false';
### Generally useful declarations
sub randElm
{$_[int rand @_]}
sub minBy (&@)
{my $f = shift;
reduce {$f->($b) < $f->($a) ? $b : $a} @_;}
sub zip
{@_ or return ();
for (my ($n, @a) = 0 ;; ++$n)
{my @row;
foreach (@_)
{$n < @$_ or return @a;
push @row, $_->[$n];}
push @a, \@row;}}
### Task-specific declarations
my $C = 100;
my $mutation_rate = .05;
my @target = split '', 'METHINKS IT IS LIKE A WEASEL';
my @valid_chars = (' ', 'A' .. 'Z');
sub fitness
{false {$_->[0] eq $_->[1]} zip shift, \@target;}
sub mutate
{my $rate = shift;
return [map {rand() < $rate ? randElm @valid_chars : $_} @{shift()}];}
### Main loop
my $parent = [map {randElm @valid_chars} @target];
while (fitness $parent)
{$parent =
minBy \&fitness,
map {mutate $mutation_rate, $parent}
1 .. $C;
print @$parent, "\n";}
Phix
with javascript_semantics constant target = "METHINKS IT IS LIKE A WEASEL", AZS = "ABCDEFGHIJKLMNOPQRSTUVWXYZ ", C = 5000, -- children in each generation P = 15 -- probability of mutation (1 in 15) function fitness(string sample, string target) return sum(sq_eq(sample,target)) end function function mutate(string s, integer n) for i=1 to length(s) do if rand(n)=1 then s[i] = AZS[rand(length(AZS))] end if end for return s end function string parent = mutate(target,1) -- (mutate with 100% probability) sequence samples = repeat(0,C) integer gen = 0, best, fit, best_fit = fitness(parent,target) while parent!=target do printf(1,"Generation%3d: %s, fitness %3.2f%%\n", {gen, parent, (best_fit/length(target))*100}) best_fit = -1 for i=1 to C do samples[i] = mutate(parent, P) fit = fitness(samples[i], target) if fit > best_fit then best_fit = fit best = i end if end for parent = samples[best] gen += 1 end while printf(1,"Finally, \"%s\"\n",{parent})
- Output:
Generation 0: NKY NWLYBJOPOJFE RRISKGJD RS, fitness 0.00% Generation 1: NKYHNNLYAIOPOJFE ERISKGJD RS, fitness 10.71% Generation 2: NKYHNNLYAIOPOJFEIER SKGJD RS, fitness 17.86% Generation 3: IKYHNNLSAIOPOJFLIER SKGJW RS, fitness 25.00% Generation 4: MKTHNNLSAIOPOJILIER SKGJW RS, fitness 32.14% Generation 5: MKTHNNLSAITFOJILIEE SKGJW RS, fitness 39.29% Generation 6: MKTHONLSAITFOJILIEE SKGJW EL, fitness 46.43% Generation 7: MKTHINLSAITFIJILIIE SKJJW EL, fitness 53.57% Generation 8: MKTHINLSAITFIS LIIE SKJJW EL, fitness 60.71% Generation 9: MKTHINLSAITFIS LIKE AKJJW EL, fitness 67.86% Generation 10: MKTHINLSAITFIS LIKE AKJEA EL, fitness 75.00% Generation 11: METHINLSAIT IS LIKE AKJEA EL, fitness 82.14% Generation 12: METHINLSAIT IS LIKE AKWEA EL, fitness 85.71% Generation 13: METHINLS IT IS LIKE AKWEA EL, fitness 89.29% Generation 14: METHINLS IT IS LIKE A WEA EL, fitness 92.86% Generation 15: METHINLS IT IS LIKE A WEASEL, fitness 96.43% Finally, "METHINKS IT IS LIKE A WEASEL"
PHP
define('TARGET','METHINKS IT IS LIKE A WEASEL');
define('TBL','ABCDEFGHIJKLMNOPQRSTUVWXYZ ');
define('MUTATE',15);
define('COPIES',30);
define('TARGET_COUNT',strlen(TARGET));
define('TBL_COUNT',strlen(TBL));
// Determine number of different chars between a and b
function unfitness($a,$b)
{
$sum=0;
for($i=0;$i<strlen($a);$i++)
if($a[$i]!=$b[$i]) $sum++;
return($sum);
}
function mutate($a)
{
$tbl=TBL;
for($i=0;$i<strlen($a);$i++) $out[$i]=mt_rand(0,MUTATE)?$a[$i]:$tbl[mt_rand(0,TBL_COUNT-1)];
return(implode('',$out));
}
$tbl=TBL;
for($i=0;$i<TARGET_COUNT;$i++) $tspec[$i]=$tbl[mt_rand(0,TBL_COUNT-1)];
$parent[0]=implode('',$tspec);
$best=TARGET_COUNT+1;
$iters=0;
do {
for($i=1;$i<COPIES;$i++) $parent[$i]=mutate($parent[0]);
for($best_i=$i=0; $i<COPIES;$i++) {
$unfit=unfitness(TARGET,$parent[$i]);
if($unfit < $best || !$i) {
$best=$unfit;
$best_i=$i;
}
}
if($best_i>0) $parent[0]=$parent[$best_i];
$iters++;
print("Generation $iters, score $best: $parent[0]\n");
} while($best);
Sample Output:
Generation 1, score 25: IIVHUVOC NRGYBUEXLF LXZ SGMT Generation 2, score 24: MIVHUVOC MKGYBUEXLF LXZ HGMT Generation 3, score 24: MIVHUVOC MKGYBUEXLF LXZ HGMT ... Generation 177, score 1: METHQNKS IT IS LIKE A WEASEL Generation 178, score 0: METHINKS IT IS LIKE A WEASEL
Picat
go =>
_ = random2(),
Target = "METHINKS IT IS LIKE A WEASEL",
Chars = "ABCDEFGHIJKLMNOPQRSTUVWXYZ ",
C = 50, % Population size in each generation
M = 80, % Mutation rate per individual in a generation (0.8)
evo(Target,Chars,C,M),
nl.
evo(Target,Chars,C,M) =>
if member(T,Target), not member(T, Chars) then
printf("The character %w is not in the character set: %w\n", T, Chars);
halt
end,
% first random string
TargetLen = Target.length,
Parent = random_chars(Chars,TargetLen),
%
% Until current fitness reaches a score of perfect match
% with the target string keep generating new populations
%
CurrentFitness = 0,
Gen = 1,
while (CurrentFitness < TargetLen)
println([gen=Gen, currentFitness=CurrentFitness, parent=Parent]),
Gen := Gen + 1,
[Parent2,CurrentFitness2] = generation(C,Chars,Target,M,Parent),
CurrentFitness := CurrentFitness2,
Parent := Parent2
end,
println([gen=Gen, currentFitness=CurrentFitness, parent=Parent]),
printf("\nFound a perfect fitness (%d) at generation %d\n", CurrentFitness, Gen),
nl.
%
% Generate a random string
%
random_chars(Chars, N) = [Chars[my_rand(Len)] : _ in 1..N] =>
Len = Chars.length.
%
% Increment the fitness for every position in the string
% S that matches the target
%
fitness(S,Target) = sum([1: I in 1..Target.length, S[I] == Target[I]]).
%
% If a random number between 1 and 100 is inside the
% bounds of mutation randomly alter a character in the string
%
mutate(S,M,Chars) = S2 =>
S2 = copy_term(S),
if my_rand(100) <= M then
S2[my_rand(S.length)] := Chars[my_rand(Chars.length)]
end.
% Get a random value between 1 and N
my_rand(N) = 1+(random() mod N).
%
% Create the next population of parent
%
generation(C,Chars,Target,M,Parent) = [NextParent,NextFitness] =>
% generate a random population
Population = [mutate(Parent,M,Chars) : _ in 1..C],
% Find the member of the population with highest fitness,
NextParent = Parent,
NextFitness = fitness(Parent,Target),
foreach(X in Population)
XF = fitness(X,Target),
if XF > NextFitness then
NextParent := X,
NextFitness := XF
end
end.
PicoLisp
This example uses 'gen', the genetic function in "lib/simul.l"
(load "@lib/simul.l")
(setq *Target (chop "METHINKS IT IS LIKE A WEASEL"))
# Generate random character
(de randChar ()
(if (=0 (rand 0 26))
" "
(char (rand `(char "A") `(char "Z"))) ) )
# Fitness function (Hamming distance)
(de fitness (A)
(cnt = A *Target) )
# Genetic algorithm
(gen
(make # Parent population
(do 100 # C = 100 children
(link
(make
(do (length *Target)
(link (randChar)) ) ) ) ) )
'((A) # Termination condition
(prinl (maxi fitness A)) # Print the fittest element
(member *Target A) ) # and check if solution is found
'((A B) # Recombination function
(mapcar
'((C D) (if (rand T) C D)) # Pick one of the chars
A B ) )
'((A) # Mutation function
(mapcar
'((C)
(if (=0 (rand 0 10)) # With a proability of 10%
(randChar) # generate a new char, otherwise
C ) ) # return the current char
A ) )
fitness ) # Selection functionOutput:
RQ ASLWWWI ANSHPNABBAJ ZLTKX DETGGNGHWITIKSXLIIEBA WAATPC CETHINWS ITKESQGIKE A WSAGHO METHBNWS IT NSQLIKE A WEAEWL METHINKS IT ISCLIKE A WVASEL METHINKS IT ISOLIKE A WEASEL METHINKS IT IS LIKE A WEASEL
Pike
C is not used because i found it has no effect on the number of mutations needed to find the solution. in difference to the proposal, rate is not set as a percentage but as the number of characters to mutate when generating an offspring.
the rate is fixed at 2 as that is the lowest most successful rate still in the spirit of the original proposal (where mutation allows a previously successful change to be undone). if the rate is 1 than every successful character change can not change again (because it would not cause an improvement and thus be rejected.)
string chars = "ABCDEFGHIJKLMNOPQRSTUVWXYZ ";
string mutate(string data, int rate)
{
array(int) alphabet=(array(int))chars;
multiset index = (multiset)enumerate(sizeof(data));
while(rate)
{
int pos = random(index);
data[pos]=random(alphabet);
rate--;
}
return data;
}
int fitness(string input, string target)
{
return `+(@`==(((array)input)[*], ((array)target)[*]));
}
void main()
{
array(string) alphabet = chars/"";
string target = "METHINKS IT IS LIKE A WEASEL";
string parent = "";
while(sizeof(parent) != sizeof(target))
{
parent += random(alphabet);
}
int count;
write(" %5d: %s\n", count, parent);
while (parent != target)
{
string child = mutate(parent, 2);
count++;
if (fitness(child, target) > fitness(parent, target))
{
write(" %5d: %s\n", count, child);
parent = child;
}
}
}
Output:
0: TIRABZB IGVG TDXTGODFOXO UPU
2: TIRABZB IGVG TDXTGO FOXOTUPU
32: TIRABZB IGVG T XTGO FOXOTUPU
39: TIRABZB IGVG T JTGO AOXOTUPU
44: TIRABNB IGMG T JTGO AOXOTUPU
57: TIRABNB IGMG T ITGO AOXOTSPU
62: TISHBNB IGMG T ITGO AOXOTSPU
63: TISHBNB IGM T ITGO AOXONSPU
74: TISHBNB GM T ITGO AOHONSPU
89: TISHBNB GM S ITGO AYHONSPU
111: TISHBNB GM S ITGO AYHOASPU
112: MISHBNB GM S ITGO AYHUASPU
145: MISHBNBG IM S ITGO AYHUASPU
169: MISHBNBG IM NS ITGO AYHEASPU
182: MESHBNBG IM NS ATGO AYHEASPU
257: MESHBNBG ID NS ATGO A HEASPU
320: MESHBNBG ID NS LRGO A HEASPU
939: MESHINBG ID NS LRGO A HEASPU
1134: MESHINBG ID NS LRZO A HEASEU
1264: MESHINBG ID US LIZO A HEASEU
1294: MEYHINBG IT US LIZO A HEASEU
1507: MEYHINBG IT US LIZO A HEASEL
1823: METHINBG IT US LIZO A HEASEL
2080: METHINBG IT US LI E A HEASEL
2143: METHINBG IT IS LI E A HEASEL
3118: METHINWG IT IS LIKE A HEASEL
3260: METHINWC IT IS LIKE A WEASEL
3558: METHINWS IT IS LIKE A WEASEL
4520: METHINKS IT IS LIKE A WEASEL
Pony
use "random"
actor Main
let _env: Env
let _rand: MT = MT // Mersenne Twister
let _target: String = "METHINKS IT IS LIKE A WEASEL"
let _possibilities: String = "ABCDEFGHIJKLMNOPQRSTUVWXYZ "
let _c: U16 = 100 // number of spawn per generation
let _min_mutate_rate: F64 = 0.09
let _perfect_fitness: USize = _target.size()
var _parent: String = ""
new create(env: Env) =>
_env = env
_parent = mutate(_target, 1.0)
var iter: U64 = 0
while not _target.eq(_parent) do
let rate: F64 = new_mutate_rate()
iter = iter + 1
if (iter % 100) == 0 then
_env.out.write(iter.string() + ": " + _parent)
_env.out.write(", fitness: " + fitness(_parent).string())
_env.out.print(", rate: " + rate.string())
end
var best_spawn = ""
var best_fit: USize = 0
var i: U16 = 0
while i < _c do
let spawn = mutate(_parent, rate)
let spawn_fitness = fitness(spawn)
if spawn_fitness > best_fit then
best_spawn = spawn
best_fit = spawn_fitness
end
i = i + 1
end
if best_fit > fitness(_parent) then
_parent = best_spawn
end
end
_env.out.print(_parent + ", " + iter.string())
fun fitness(trial: String): USize =>
var ret_val: USize = 0
var i: USize = 0
while i < trial.size() do
try
if trial(i)? == _target(i)? then
ret_val = ret_val + 1
end
end
i = i + 1
end
ret_val
fun new_mutate_rate(): F64 =>
let perfect_fit = _perfect_fitness.f64()
((perfect_fit - fitness(_parent).f64()) / perfect_fit) * (1.0 - _min_mutate_rate)
fun ref mutate(parent: String box, rate: F64): String =>
var ret_val = recover trn String end
for char in parent.values() do
let rnd_real: F64 = _rand.real()
if rnd_real <= rate then
let rnd_int: U64 = _rand.int(_possibilities.size().u64())
try
ret_val.push(_possibilities(rnd_int.usize())?)
end
else
ret_val.push(char)
end
end
consume ret_val
Output:
100: UMMMDNKR IEIIB IIKZ A THAHEL, fitness: 14, rate: 0.455 200: UMMMDNKR IEIIB IIKZ A THAHEL, fitness: 14, rate: 0.455 300: KMHJZNKS IUIIS IISQ A TWASEL, fitness: 16, rate: 0.39 400: KHHHCNKS IT I CIKE A XFASEL, fitness: 20, rate: 0.26 500: MINHINKS IT IS LIKE A WEASEL, fitness: 26, rate: 0.065 METHINKS IT IS LIKE A WEASEL, 526
Alternative solution:
Using a more OO approach that leverages classes for encapsulation.
use "random"
use "collections"
class CreationFactory
let _desired: String
new create(d: String) =>
_desired = d
fun apply(c: String): Creation =>
Creation(c, _fitness(c))
fun _fitness(s: String): USize =>
var f = USize(0)
for i in Range(0, s.size()) do
try
if s(i)? == _desired(i)? then
f = f +1
end
end
end
f
class val Creation
let string: String
let fitness: USize
new val create(s: String = "", f: USize = 0) =>
string = s
fitness = f
class Mutator
embed _rand: MT = MT
let _possibilities: String = "ABCDEFGHIJKLMNOPQRSTUVWXYZ "
let _cf: CreationFactory
new create(cf: CreationFactory) =>
_cf = cf
fun ref apply(parent: Creation, rate: F64): Creation =>
let ns = _new_string(parent.string, rate)
_cf(ns)
fun ref _new_string(parent: String, rate: F64): String =>
var mutated = recover String(parent.size()) end
for char in parent.values() do
mutated.push(_mutate_letter(char, rate))
end
consume mutated
fun ref _mutate_letter(current: U8, rate: F64): U8 =>
if _rand.real() <= rate then
_random_letter()
else
current
end
fun ref _random_letter(): U8 =>
let ln = _rand.int(_possibilities.size().u64()).usize()
try _possibilities(ln)? else ' ' end
class Generation
let _size: USize
let _desired: Creation
let _mutator: Mutator
new create(size: USize = 100, desired: Creation, mutator: Mutator) =>
_size = size
_desired = desired
_mutator = consume mutator
fun ref apply(parent: Creation): Creation =>
var best = parent
let mutation_rate = _mutation_rate(best)
for i in Range(0, _size) do
let candidate = _mutator(best, mutation_rate)
if candidate.fitness > best.fitness then
best = candidate
end
end
best
fun _mutation_rate(best: Creation): F64 =>
let min_mutate_rate: F64 = 0.09
let df = _desired.fitness.f64()
let bf = best.fitness.f64()
((df - bf) / df) * (1.0 - min_mutate_rate)
actor Main
new create(env: Env) =>
let d = "METHINKS IT IS LIKE A WEASEL"
let cf = CreationFactory(d)
let desired = cf(d)
let mutator = Mutator(cf)
let start = mutator(desired, 1.0)
let spawn_per_generation = USize(100)
var iterations = U64(0)
var best = start
repeat
best = Generation(spawn_per_generation, desired, mutator)(best)
iterations = iterations + 1
if (iterations % 100) == 0 then
env.out.print(
iterations.string() + ": "
+ best.string + ", fitness: " + best.fitness.string()
)
end
until best.string == desired.string end
env.out.print(best.string + ", " + iterations.string())
Output:
100: MELWILYSH TDKKTPIKE DXWEASKL, fitness: 14 200: MEMHINTSLLT M KPFKETN WEASHL, fitness: 16 300: MQTHINFS ET MT DIKEVA WEASEL, fitness: 21 400: METHINKS IT IS DIKEDA WEASEL, fitness: 26 METHINKS IT IS LIKE A WEASEL, 442
Prolog
target("METHINKS IT IS LIKE A WEASEL").
rndAlpha(64, 32). % Generate a single random character
rndAlpha(P, P). % 32 is a space, and 65->90 are upper case
rndAlpha(Ch) :- random(N), P is truncate(64+(N*27)), !, rndAlpha(P, Ch).
rndTxt(0, []). % Generate some random text (fixed length)
rndTxt(Len, [H|T]) :- succ(L, Len), rndAlpha(H), !, rndTxt(L, T).
score([], [], Score, Score). % Score a generated mutation (count diffs)
score([Ht|Tt], [Ht|Tp], C, Score) :- !, score(Tt, Tp, C, Score).
score([_|Tt], [_|Tp], C, Score) :- succ(C, N), !, score(Tt, Tp, N, Score).
score(Txt, Score, Target) :- !, score(Target, Txt, 0, Score).
mutate(_, [], []). % mutate(Probability, Input, Output)
mutate(P, [H|Txt], [H|Mut]) :- random(R), R < P, !, mutate(P, Txt, Mut).
mutate(P, [_|Txt], [M|Mut]) :- rndAlpha(M), !, mutate(P, Txt, Mut).
weasel(Tries, _, _, mutation(0, Result)) :- % No differences=success
format('~w~4|:~w~3| - ~s\n', [Tries, 0, Result]).
weasel(Tries, Chance, Target, mutation(S, Value)) :- % output progress
format('~w~4|:~w~3| - ~s\n', [Tries, S, Value]), !, % and call again
weasel(Tries, Chance, Target, Value).
weasel(Tries, Chance, Target, Start) :-
findall(mutation(S,M), % Generate 30 mutations, select the best.
(between(1, 30, _), mutate(Chance, Start, M), score(M,S,Target)),
Mutations), % List of 30 mutations and their scores
sort(Mutations, [Best|_]), succ(Tries, N),
!, weasel(N, Chance, Target, Best).
weasel :- % Chance->probability for a mutation, T=Target, Start=initial text
target(T), length(T, Len), rndTxt(Len, Start), Chance is 1 - (1/(Len+1)),
!, weasel(0, Chance, T, Start).
Output:
time(weasel). 1 :27 - SGR JDTLWJQNGFOEJNQTVQOJLEEV 2 :27 - SGR DDTLWJQNGFOEJNQTVQOJLEEV 3 :26 - SGR DDTLWJQNGFHEJNQTVQOJLSEV 4 :25 - MGR DDWLWJQNGFHEJDQTVQOJLSEV 5 :24 - MGR DDWL JQNGFHEJDQTVQOJLSEV 6 :24 - MGR DBWL JQNGFHEJUQTVQOJLSEV 7 :23 - MRR IBWL JQNGFHEJUQTVFOJLSEV ... 168 :1 - METHINKS IT I LIKE A WEASEL 169 :1 - METHINKS IT I LIKE A WEASEL 170 :1 - METHINKS IT I LIKE A WEASEL 171 :1 - METHINKS IT I LIKE A WEASEL 172 :1 - METHINKS IT I LIKE A WEASEL 173 :0 - METHINKS IT IS LIKE A WEASEL % 810,429 inferences, 0.125 CPU in 0.190 seconds (66% CPU, 6493780 Lips) true
PureBasic
Define population = 100, mutationRate = 6
Define.s target$ = "METHINKS IT IS LIKE A WEASEL"
Define.s charSet$ = "ABCDEFGHIJKLMNOPQRSTUVWXYZ "
Procedure.i fitness(Array aspirant.c(1), Array target.c(1))
Protected i, len, fit
len = ArraySize(aspirant())
For i = 0 To len
If aspirant(i) = target(i): fit +1: EndIf
Next
ProcedureReturn fit
EndProcedure
Procedure mutatae(Array parent.c(1), Array child.c(1), Array charSetA.c(1), rate.i)
Protected i, L, maxC
L = ArraySize(child())
maxC = ArraySize(charSetA())
For i = 0 To L
If Random(100) < rate
child(i) = charSetA(Random(maxC))
Else
child(i) = parent(i)
EndIf
Next
EndProcedure
Procedure.s cArray2string(Array A.c(1))
Protected S.s, len
len = ArraySize(A())+1 : S = Space(len)
CopyMemory(@A(0), @S, len * SizeOf(Character))
ProcedureReturn S
EndProcedure
Define mutationRate, maxChar, target_len, i, maxfit, gen, fit, bestfit
Dim targetA.c(Len(target$) - 1)
CopyMemory(@target$, @targetA(0), StringByteLength(target$))
Dim charSetA.c(Len(charSet$) - 1)
CopyMemory(@charSet$, @charSetA(0), StringByteLength(charSet$))
maxChar = Len(charSet$) - 1
maxfit = Len(target$)
target_len = Len(target$) - 1
Dim parent.c(target_len)
Dim child.c(target_len)
Dim Bestchild.c(target_len)
For i = 0 To target_len
parent(i) = charSetA(Random(maxChar))
Next
fit = fitness (parent(), targetA())
OpenConsole()
PrintN(Str(gen) + ": " + cArray2string(parent()) + ": Fitness= " + Str(fit) + "/" + Str(maxfit))
While bestfit <> maxfit
gen + 1
For i = 1 To population
mutatae(parent(),child(),charSetA(), mutationRate)
fit = fitness (child(), targetA())
If fit > bestfit
bestfit = fit: CopyArray(child(), Bestchild())
EndIf
Next
CopyArray(Bestchild(), parent())
PrintN(Str(gen) + ": " + cArray2string(parent()) + ": Fitness= " + Str(bestfit) + "/" + Str(maxfit))
Wend
PrintN("Press any key to exit"): Repeat: Until Inkey() <> ""
Python
Using lists instead of strings for easier manipulation, and a mutation rate that gives more mutations the further the parent is away from the target.
from string import letters
from random import choice, random
target = list("METHINKS IT IS LIKE A WEASEL")
charset = letters + ' '
parent = [choice(charset) for _ in range(len(target))]
minmutaterate = .09
C = range(100)
perfectfitness = float(len(target))
def fitness(trial):
'Sum of matching chars by position'
return sum(t==h for t,h in zip(trial, target))
def mutaterate():
'Less mutation the closer the fit of the parent'
return 1-((perfectfitness - fitness(parent)) / perfectfitness * (1 - minmutaterate))
def mutate(parent, rate):
return [(ch if random() <= rate else choice(charset)) for ch in parent]
def que():
'(from the favourite saying of Manuel in Fawlty Towers)'
print ("#%-4i, fitness: %4.1f%%, '%s'" %
(iterations, fitness(parent)*100./perfectfitness, ''.join(parent)))
def mate(a, b):
place = 0
if choice(xrange(10)) < 7:
place = choice(xrange(len(target)))
else:
return a, b
return a, b, a[:place] + b[place:], b[:place] + a[place:]
iterations = 0
center = len(C)/2
while parent != target:
rate = mutaterate()
iterations += 1
if iterations % 100 == 0: que()
copies = [ mutate(parent, rate) for _ in C ] + [parent]
parent1 = max(copies[:center], key=fitness)
parent2 = max(copies[center:], key=fitness)
parent = max(mate(parent1, parent2), key=fitness)
que()
Sample output
#100 , fitness: 50.0%, 'DVTAIKKS OZ IAPYIKWXALWE CEL' #200 , fitness: 60.7%, 'MHUBINKMEIG IS LIZEVA WEOPOL' #300 , fitness: 71.4%, 'MEYHINKS ID SS LIJF A KEKUEL' #378 , fitness: 100.0%, 'METHINKS IT IS LIKE A WEASEL'
A simpler Python version that converges in less steps:
from random import choice, random
target = list("METHINKS IT IS LIKE A WEASEL")
alphabet = " ABCDEFGHIJLKLMNOPQRSTUVWXYZ"
p = 0.05 # mutation probability
c = 100 # number of children in each generation
def neg_fitness(trial):
return sum(t != h for t,h in zip(trial, target))
def mutate(parent):
return [(choice(alphabet) if random() < p else ch) for ch in parent]
parent = [choice(alphabet) for _ in xrange(len(target))]
i = 0
print "%3d" % i, "".join(parent)
while parent != target:
copies = (mutate(parent) for _ in xrange(c))
parent = min(copies, key=neg_fitness)
print "%3d" % i, "".join(parent)
i += 1
R
set.seed(1234, kind="Mersenne-Twister")
## Easier if the string is a character vector
target <- unlist(strsplit("METHINKS IT IS LIKE A WEASEL", ""))
charset <- c(LETTERS, " ")
parent <- sample(charset, length(target), replace=TRUE)
mutaterate <- 0.01
## Number of offspring in each generation
C <- 100
## Hamming distance between strings normalized by string length is used
## as the fitness function.
fitness <- function(parent, target) {
sum(parent == target) / length(target)
}
mutate <- function(parent, rate, charset) {
p <- runif(length(parent))
nMutants <- sum(p < rate)
if (nMutants) {
parent[ p < rate ] <- sample(charset, nMutants, replace=TRUE)
}
parent
}
evolve <- function(parent, mutate, fitness, C, mutaterate, charset) {
children <- replicate(C, mutate(parent, mutaterate, charset),
simplify=FALSE)
children <- c(list(parent), children)
children[[which.max(sapply(children, fitness, target=target))]]
}
.printGen <- function(parent, target, gen) {
cat(format(i, width=3),
formatC(fitness(parent, target), digits=2, format="f"),
paste(parent, collapse=""), "\n")
}
i <- 0
.printGen(parent, target, i)
while ( ! all(parent == target)) {
i <- i + 1
parent <- evolve(parent, mutate, fitness, C, mutaterate, charset)
if (i %% 20 == 0) {
.printGen(parent, target, i)
}
}
.printGen(parent, target, i)
output:
0 0.00 DQQQXRAGRNSOHYHWHHFGIIEBFVOY 20 0.36 MQQQXBAS TTOHSHLHKF I ABFSOY 40 0.71 MQTHINKS TTXHSHLIKE A WBFSEY 60 0.82 METHINKS IT HSHLIKE A WBFSEY 80 0.93 METHINKS IT HS LIKE A WEFSEL 99 1.00 METHINKS IT IS LIKE A WEASEL
Alternative
Very close to former solution, but a bit easier.
# Setup
set.seed(42)
target= unlist(strsplit("METHINKS IT IS LIKE A WEASEL", ""))
chars= c(LETTERS, " ")
C= 100
# Fitness function; high value means higher fitness
fitness= function(x){
sum(x == target)
}
# Mutate function
mutate= function(x, rate= 0.01){
idx= which(runif(length(target)) <= rate)
x[idx]= replicate(n= length(idx), expr= sample(x= chars, size= 1, replace= T))
x
}
# Evolve function
evolve= function(x){
parents= rep(list(x), C+1) # Repliction
parents[1:C]= lapply(parents[1:C], function(x) mutate(x)) # Mutation
idx= which.max(lapply(parents, function(x) fitness(x))) # Selection
parents[[idx]]
}
# Initialize first parent
parent= sample(x= chars, size= length(target), replace= T)
# Main program
while (fitness(parent) < fitness(target)) {
parent= evolve(parent)
cat(paste0(parent, collapse=""), "\n")
}
output:
YEHWROTERTMEZGMZ DMPYD ZCNKY ... METHINKS IT IS LIKE A WEASEL
Racket
#lang racket
(define alphabet " ABCDEFGHIJKLMNOPQRSTUVWXYZ")
(define (randch) (string-ref alphabet (random 27)))
(define (fitness s1 s2)
(for/sum ([c1 (in-string s1)] [c2 (in-string s2)])
(if (eq? c1 c2) 1 0)))
(define (mutate s P)
(define r (string-copy s))
(for ([i (in-range (string-length r))] #:when (<= (random) P))
(string-set! r i (randch)))
r)
(define (evolution target C P)
(let loop ([parent (mutate target 1.0)] [n 0])
;; (printf "~a: ~a\n" n parent)
(if (equal? parent target)
n
(let cloop ([children (for/list ([i (in-range C)]) (mutate parent P))]
[best #f] [fit -1])
(if (null? children)
(loop best (add1 n))
(let ([f (fitness target (car children))])
(if (> f fit)
(cloop (cdr children) (car children) f)
(cloop (cdr children) best fit))))))))
;; Some random experiment using all of this
(define (try-run C P)
(define ns
(for/list ([i 10])
(evolution "METHINKS IT IS LIKE A WEASEL" C P)))
(printf "~s Average generation: ~s\n" C (/ (apply + 0.0 ns) (length ns)))
(printf "~s Total strings: ~s\n" C (for/sum ([n ns]) (* n 50))))
(for ([C (in-range 10 501 10)]) (try-run C 0.001))
Raku
(formerly Perl 6)
constant target = "METHINKS IT IS LIKE A WEASEL";
constant @alphabet = flat 'A'..'Z',' ';
constant C = 10;
sub mutate(Str $string, Real $mutate-chance where 0 ≤ * < 1) {
$string.subst: /<?{ rand < $mutate-chance }> . /, @alphabet.pick, :global
}
sub fitness(Str $string) { [+] $string.comb Zeq target.comb }
printf "\r%6d: '%s'", $++, $_ for
@alphabet.roll(target.chars).join,
{ max :by(&fitness), mutate($_, .001) xx C } ... target;
print "\n";
Red
Red[]
; allowed characters
alphabet: "ABCDEFGHIJKLMNOPQRSTUVWXYZ "
; target string
target: "METHINKS IT IS LIKE A WEASEL"
; parameter controlling the number of children
C: 10
; parameter controlling the evolution rate
RATE: 0.05
; compute closeness of 'string' to 'target'
fitness: function [string] [
sum: 0
repeat i length? string [
if not-equal? pick string i pick target i [
sum: sum + 1
]
]
sum
]
; return copy of 'string' with mutations, frequency based on given 'rate'
mutate: function [string rate] [
result: copy string
repeat i length? result [
if rate > random 1.0 [
poke result i random/only alphabet
]
]
result
]
; create initial random parent
parent: ""
repeat i length? target [
append parent random/only alphabet
]
; main loop, displaying progress
while [not-equal? parent target] [
print parent
children: copy []
repeat i C [
append children mutate parent RATE
]
sort/compare children function [a b] [lesser? fitness a fitness b]
parent: pick children 1
]
print parent
REXX
optimized
This REXX version:
- allows random seed for repeatability of runs
- allows mutation rate to be expressed as a percentage (%)
- echoes specification(s) and target string
- columnar alignment of output
- optimized for speed (only one random number/mutation)
- supports an alphabet with lowercase letters and other letters and/or punctuation.
/*REXX program demonstrates an evolutionary algorithm (by using mutation). */
parse arg children MR seed . /*get optional arguments from the C.L. */
if children=='' | children=="," then children=10 /*# children produced each generation. */
if MR =='' | MR =="," then MR= "4%" /*the character Mutation Rate each gen.*/
if right(MR,1)=='%' then MR= strip(MR,,"%")/100 /*expressed as a percent? Then adjust.*/
if seed\=='' then call random ,,seed /*SEED allow the runs to be repeatable.*/
abc = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ ' ; Labc= length(abc)
target= 'METHINKS IT IS LIKE A WEASEL' ; Ltar= length(target)
parent= mutate( left('', Ltar), 1) /*gen rand string,same length as target*/
say center('target string', Ltar, "─") 'children' "mutationRate"
say target center(children, 8) center((MR*100/1)'%', 12); say
say center('new string' ,Ltar, "─") "closeness" 'generation'
do gen=0 until parent==target; close= fitness(parent)
almost= parent
do children; child= mutate(parent,MR)
_= fitness(child); if _<=close then iterate
close= _; almost= child
say almost right(close, 9) right(gen, 10)
end /*children*/
parent= almost
end /*gen*/
exit /*stick a fork in it, we're all done. */
/*──────────────────────────────────────────────────────────────────────────────────────*/
fitness: parse arg x; $=0; do k=1 for Ltar; $= $+(substr(x,k,1)==substr(target,k,1)); end
return $
/*──────────────────────────────────────────────────────────────────────────────────────*/
mutate: parse arg x,rate; $= /*set X to 1st argument, RATE to 2nd.*/
do j=1 for Ltar; r= random(1, 100000) /*REXX's max for RANDOM*/
if .00001*r<=rate then $= $ || substr(abc, r//Labc+1, 1)
else $= $ || substr(x , j , 1)
end /*j*/
return $
- output when using the following input: 20 4% 11
───────target string──────── children mutationRate METHINKS IT IS LIKE A WEASEL 20 4% ─────────new string───────── closeness generation TWLPLGNVVMXFBUKHUPEQXOCUPIUS 1 0 TWLPLGNVVMXFBU HUPEQXOCUPIUS 2 1 TWLPLGNVVMX BU HUPEQXOCUPIUS 3 2 TWLPLCNVFMX BP HUPEQAOCUPIUS 4 4 TWLPLQNVFMX BP HUPEQAOCUPGUL 5 6 TWLHLQNVFMX BS HUPEQAOUUPGUL 7 9 RWLHLQNZFMX BS HUPEQAOUUEGEL 8 14 RWLHLQNZFIX BS HUPEQAOUUEGEL 9 15 RWLHLQNZFIX BS HUPE AOUUEGEL 10 19 RWLHLQNZFIX BS LWPE AOUUEGEL 11 22 RWLHLQNZFIX BS LWPE A UUEGEL 12 28 RWLHLNNZFIX BS LWPE A UUEGEL 13 36 RELHLNNZFIX BE LWPE A UUAGEL 14 40 RELHLNNZFIX BE LWPE A UUASEL 15 43 RELHLNNZFIX BE LWKE A UASEL 16 50 RELHLNNZFIT BE LWKE A UASEL 17 62 RELHLNNSFIT IE LWKE A UASEL 19 67 RETHLNNSFIT IE LWKE A UASEL 20 71 RETHLNNSFIT IE LIKE A UASEL 21 79 METHLNNSFIT IE LIKE A LASEL 22 91 METHLNNSFIT IE LIKE A WLASEL 23 112 METHLNNSFIT IE LIKE A WEASEL 24 144 METHLNNS IT IE LIKE A WEASEL 25 151 METHLNKS IT IM LIKE A WEASEL 26 160 METHLNKS IT IS LIKE A WEASEL 27 164 METHINKS IT IS LIKE A WEASEL 28 170
optimized, stemmed arrays
This REXX version uses stemmed arrays for the character-by-character comparison [T.n] as well as
generating a random character [@.n] during mutation, thus making it slightly faster (about 10%),
especially for a longer string and/or a low mutation rate.
/*REXX program demonstrates an evolutionary algorithm (by using mutation). */
parse arg children MR seed . /*get optional arguments from the C.L. */
if children=='' | children=="," then children=10 /*# children produced each generation. */
if MR =='' | MR =="," then MR= "4%" /*the character Mutation Rate each gen.*/
if right(MR,1)=='%' then MR= strip(MR,,"%")/100 /*expressed as a percent? Then adjust.*/
if seed\=='' then call random ,,seed /*SEED allow the runs to be repeatable.*/
abc = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ '; Labc= length(abc)
do i=0 for Labc; @.i= substr(abc, i+1, 1) /*define array (for faster compare), */
end /*i*/ /*than picking one from a char string. */
target= 'METHINKS IT IS LIKE A WEASEL' ; Ltar= length(target)
do j=1 for Ltar; T.j= substr(target, j, 1) /*define an array (for faster compare),*/
end /*j*/ /*faster than a byte-by-byte compare. */
parent= mutate( left('', Ltar), 1) /*gen rand string, same length as tar. */
say center('target string', Ltar, "─") 'children' "mutationRate"
say target center(children, 8) center( (MR*100/1)'%', 12); say
say center('new string' , Ltar, "─") 'closeness' "generation"
do gen=0 until parent==target; close= fitness(parent)
almost= parent
do children; child= mutate(parent, MR)
_= fitness(child); if _<=close then iterate
close= _; almost= child
say almost right(close, 9) right(gen, 10)
end /*children*/
parent= almost
end /*gen*/
exit /*stick a fork in it, we're all done. */
/*──────────────────────────────────────────────────────────────────────────────────────*/
fitness: parse arg x; $=0; do k=1 for Ltar; $=$+(substr(x,k,1)==T.k); end; return $
/*──────────────────────────────────────────────────────────────────────────────────────*/
mutate: parse arg x,rate /*set X to 1st argument, RATE to 2nd.*/
$=; do m=1 for Ltar; r=random(1, 100000) /*REXX's max for RANDOM*/
if .00001*r<=rate then do; _= r//Labc; $= $ || @._; end
else $= $ || substr(x, m, 1)
end /*m*/
return $
- output is the same as the previous version.
Ring
# Project : Evolutionary algorithm
target = "METHINKS IT IS LIKE A WEASEL"
parent = "IU RFSGJABGOLYWF XSMFXNIABKT"
num = 0
mutationrate = 0.5
children = len(target)
child = list(children)
while parent != target
bestfitness = 0
bestindex = 0
for index = 1 to children
child[index] = mutate(parent, mutationrate)
fitness = fitness(target, child[index])
if fitness > bestfitness
bestfitness = fitness
bestindex = index
ok
next
if bestindex > 0
parent = child[bestindex]
num = num + 1
see "" + num + ": " + parent + nl
ok
end
func fitness(text, ref)
f = 0
for i = 1 to len(text)
if substr(text, i, 1) = substr(ref, i, 1)
f = f + 1
ok
next
return (f / len(text))
func mutate(text, rate)
rnd = randomf()
if rate > rnd
c = 63+random(27)
if c = 64
c = 32
ok
rnd2 = random(len(text))
if rnd2 > 0
text[rnd2] = char(c)
ok
ok
return text
func randomf()
decimals(10)
str = "0."
for i = 1 to 10
nr = random(9)
str = str + string(nr)
next
return number(str)Output:
1: IU RFPGJABGOLYWF XSMFXNIABKT 2: IU RFPGJABGOLQWF XSMFXNIABKT 3: IU RFPGJABGOLQWF XSMAXNIABKT 4: IU RFPGJABGOLQWF XSMA NIABKT 5: IU RFPGJABGOLQWF XSMA NIABKT 6: IU RFPGJABGOLSWF XSMA NIABKT 7: IU RFPGJABGOLSWF XSMA NIABKT 8: IUTRFPGJABGOLSWF XSMA NIABKT 9: IUTRFPGSABGOLSWF XSMA NIABKT 10: IUTRFPGSABGOLSWF XSMA NIABKT 11: IUTRFPGSABGOLSWF XSMA NIABKT 12: IUTRFPGSABGOLSWF XSMA NIABKT 13: IUTRFPGSABGOLSWF XSMA NIABKE 14: IUTRFPGSABGOMSWF XSMA NIABKE 15: IUTRFPGSYBGOMSWF XSMA NIABKE 16: IUTRFPGSYBGOMSWF XSMA NIABKE 17: IUTRFPGSYBGOMSWF XSMA NIABKE 18: IUTRFPGSYBGOMSWF XSMA NIASKE 19: IUTRFPGSYBGOMSWF XSMA NIASKE 20: IUTRFPGSYMGOMSWF XSMA NIASKE 21: IUTRFPGSYMGOMSWF XSMA NIASKE 22: IUTRFPGSYMGOMSW