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Talk:Evolutionary algorithm: Difference between revisions
→Are the Python and C++ solution cheating?
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: Looks like it's within the letter of the task, but as to the spirit, I don't know. Intuition tells me that knowing the true distance to optimal will help avoid problems of local minima, and some (but certainly not all!) problems that evolutionary algorithms are applied to have true distance (or a close approximation of such) available. This may be a good case for splitting the task and specifying a goal-agnostic algorithm. --[[User:Short Circuit|Michael Mol]] 16:19, 1 September 2010 (UTC)
:: in the pike example i used to start the rate at the length of the string, decreasing the value gradually, but i found that whatever rate was used the result was the same. so now the code uses a rate of changing one character per mutation. also as is noted below about scalability, a high rate is never useful. so the rate seems actually not important to the problem.--[[User:EMBee|eMBee]] 08:57, 11 October 2011 (UTC)
: In a real problem, you've got a high-dimensional space that you're searching and the fitness function is only poorly known (the profusion of species is clear demonstration that there are many local minima in the problem space that is biology). However, the only effect of varying the mutation rate with fitness, given that we have a reasonable metric, is that it results in faster convergence with smaller populations at each step. It doesn't change the fact that you're ''still'' having to do the evolution towards a solution through random variation and selection, which is the whole point. –[[User:Dkf|Donal Fellows]] 08:26, 2 September 2010 (UTC)
:: +1. (But then I would :-) --[[User:Paddy3118|Paddy3118]] 21:36, 1 October 2011 (UTC)
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