I\'m programming Genetic Algorithm in C++ and after searching all kind of ways o
ID: 658570 • Letter: I
Question
I'm programming Genetic Algorithm in C++ and after searching all kind of ways of doing GA'a operators (selection, crossover, mutation) I came up with a doubt.
Let's say I have an initial population of 500. My selection will consist in getting the top 20% of 500(based on best fitness). So I get 100 individuals to mate. When I do the crossover I'll get 2 children where both together have 50% of surviving. So far so good. I start the mutation, and everything's ok.. Now when I start choosing the Next generation, I see that I have a big number of children (in this case, 4950 if you wanna know). Now the thing is, every time I run GA, if I send all the children to the next generation, the number of individuals per generation will increase exponentially.
So there must be a way of choosing the children to fulfill a new generation without getting out of this range of the initial population.
What I'm asking here is if there is anyway of choosing the children to fill the new generations OR should I choose somehow (and maybe reduce) the parents to mate so I don't get so many children in the end.
Explanation / Answer
After that you have generated your initial population (the pool should be quite large) and you apply your fitness function to it, you select your parents for the next generation.
Once that you have your parents, you discard the other individuals so that you can replace them with the new generation. This replacing will keep your population size in control, after all, if individual I did not fit the fitness criteria to contribute to the next generation, why do you need to keep it?
Note however, that in your study, there is a high chance that your algorithm will converge to a local maxima/minima very quickly. This is because you only keep the top 20% of your population for mating. This is usually a bad idea since it will get your GA stuck in a local maxima/minima. To fix this, you would also include some of worse solutions as well, say for instance, the top 20% and the worse 5-10%.
EDIT: Alternatively, you could also go for something akin to what @ Jeff Langemeier proposes and instead of selecting the worse 10% of the population, you randomly select a given amount from the non best (in this case, the remaining 80%) individual.