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Adaptive Mutations / Crossovers for Genetic Algorithms

I am studying how I can implement the adaptive mutation rate for the genetic algorithm I'm working on. I have seen an algorithm that uses the current physical form and the average ability of the population to calculate the rate of mutation, however, I am not sure that it is very effective.

In the algorithm I saw, you do the following:

mutationRate = (bestFitness - individualFitness) / (bestFitness - averageFitness) * 0.5

Would this be a good approach or better?

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I do not think there is a “better way”: the mutation algorithm and mutation rate are specific to a particular algorithm.

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  • - Dirk Thierens
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