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Yesterday I've done some research how to optimize genetic algorithm and I've encountered a very interesting theory that we can use Lamarckian theory (adaptive theory) to optimize the neural network. But I still didn't understand how can it be done? What I've read from some source to optimize the genetic algorithm we must do some adaptation to the parents before we do the mating (crossover & mutation) but how is exactly the adaptation done? For example, if I have a genetic algorithm population to train a neural network, how can I alter the chromosome of the population to be adapted to the environment? Is it based on the error of the output or how?

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I can certainly envision genetic algorithms in which the members of the population are somehow tweaked to make them "better". Perhaps the mutation technique of looking for "nearby" genotypes that are better should be described this way. As the whole GA area is really an extremely varied collection of ad hoc techniques with scanty theoretical underpinning, I'd just say try anything that looks reasonable. – vonbrand Jan 27 at 4:08
I see I see, I also thought about looking for "nearby" genotypes that are better and try to mutate to that way but I thought the population diversity will be decreased, what do you think about it? – NAYOSO Jan 27 at 4:59
You are interested in the best in the population, so this isn't too bad. Perhaps do it as a fraction of the mutations? So you have yet another parameter to frob! – vonbrand Jan 27 at 5:02
Ah I see, I'll try to research further about it after I finish with my thesis examination, thank you vonbrand! – NAYOSO Jan 27 at 7:55
good luck with your examination! – vonbrand Jan 27 at 17:31
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