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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