Evolutionary algorithm: Difference between revisions
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Generation 178, score 0: METHINKS IT IS LIKE A WEASEL
</pre>
=={{header|Picat}}==
<lang 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.</lang>
=={{header|PicoLisp}}==
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