Evolutionary Algorithms: Optimal Repopulation Brea

Question

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!

Answer 1

You know what you could do... You could write a genetic algorithm to determine that optimal distribution.

But, usually I keep the top 12%, and 28% crossbreeds; with 30% each for the others.

Answer 2

There would appear to be a few answers suggesting using a 2nd GA to determine optimum parameters for the 1st GA, with no mention of how to determine the optimum parameters for the 2nd. I can't help but wonder about the religious beliefs of those suggesting this approach...