It's really all in the title, but here's a breakdown for anyone who is interested in Evolutionary Algorithms:
In an EA, the basic premise is that you randomly generate a certain number of organisms (which are really just sets of parameters), run them against a problem, and then let the top performers survive.
You then repopulate with a combination of crossbreeds of the survivors, mutations of the survivors, and also a certain number of new random organisms.
Do that several thousand times, and efficient organisms arise.
Some people also do things like introduce multiple "islands" of organisms, which are seperate populations that are allowed to crossbreed once in awhile.
So, my question is: what are the optimal repopulation percentages?
I have been keeping the top 10% performers, and repopulating with 30% crossbreeds and 30% mutations. The remaining 30% is for new organisms.
I have also tried out the multiple island theory, and I'm interested in your results on that as well.
It is not lost on me that this is exactly the type of problem an EA could solve. Are you aware of anyone trying that?
Thanks in advance!
I initially tried to model what I thought organic systems were like. Ultimately decided that was no good, and went more aggressive, with 10% kept, 20% mutated, 60% crossbred, and 10% random.
Then I noticed my top 10% were all roughly identical. So I increased the random to 30%. That helped some, but not much.
I did try multiple island, and generation-skipping, and reseeding, which gave better results, but still highly unsatisfactory, very little variation in the top 10%, crazy-long numbers of generations to get any results. Mostly the code learned how to hack my fitness evaluation.
It's really easy to get top performers, so don't worry about keeping too many of them around. Crossbreeds help to pare down positive and negative traits, so they're useful, but really what you want to get is a lot of good random bred in. Focus on mutations and new randoms to bring in features, and let the crossbreeds and top performers just keep track of the best and refine them more slowly. IE: stuff based on the last generation is just finding a better local maxima, randoms find better global maxima.
I still believe optimal answers to your question can be found by observing natural phenomena, such as in a recent article regarding randomness of fruit-fly flight paths, so that may pan out.
Probably the best answer is to just run it and tweak it, don't be afraid to tweak it pretty heavily, the populations are robust. Make sure you implement a way to save and continue.