Re: [jgap-users] keep population evolving

[Laszlo I. <ill...@sa...>]

Hi Kurt,

This is a problem of the structure of your problem I think.
To not lock in a solution You have to increase the mutation probability.
But if do so, it came a time when You are in the random search situation.
The genetic algorithm has to converge to a solution for all members, not if You are in genetic drift situation when two good solutions alternate and have similar structure and similar value of the fitness.

Solutions are many.
0. Store the best solutions in a solution pool. (for all runs)

1. Start with a high mutation probability for 20-30 generations (depends on problem), let it be (20-40%) and decrease this probability after.
2. Start with a high mutation probability and change it over generations.

I don't know if it is possible in jgap, I use it 5 years ago.

Mutation explores the solution space.
Crossover is the operator that makes the convergence of population.

Regards

Laszlo

----- Original Message -----
From: Kurt Whittemore <kur...@gm...>
To: jga...@li...
Sent: Mon, 21 Jul 2014 02:23:31 +0300 (EEST)
Subject: [jgap-users] keep population evolving

I'm using jgap to evolve some solutions.  If a solution has already been found, then I assign a really low fitness value because I want to find the maximum number of different "good" solutions.  After several generations, all of the individuals in my population have the same configuration and they don't change, but they all have a low fitness score.  I thought that the population would just keep randomly moving around if all of the individuals had a low fitness score, but they just seem to stay the same  How should I prevent the population from becoming "locked" like this.

For now, I think I will just kind of have my program manually change a chromosome to a random configuration if the lowest fitness value is assigned.  I'm just curious if there are other solutions though.

Thanks for any help and information you have to offer!

Best,
Kurt