Optimal Mutation and Crossover Rates for a Genetic Algorithm Operating in a Dynamic Environment

We attempt to find mutation / crossover rate pairs that facilitate the performance of a genetic algorithm (GA) on a simple dynamic fitness function. This research results in two products. The first is a dynamic fitness function that is founded in previous analysis done on both static and dynamic landscapes, and that avoids problematic issues with previously proposed dynamic landscapes for GAs. The second is a general relationship between the crossover and mutation rates that are most useful for a dynamic fitness function with a specific rate of change in Hamming distance, and that could possibly provide insight into the utility of the standard GA approach for the optimization of dynamic landscapes.

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