Analysis of possible genome-dependence of mutation rates in genetic algorithms

Abstract

The performance of the evolutionary algorithms depends strongly upon the combined effect of the operators (e.g. mutation) and the mappings from genotype to phenotype space and phenotype to fitness space. We demonstrate, with the example of the canonical Genetic Algorithm (cGA) for parameter optimization, that the right choice of the mutation operator should depend on the genom position and we show that generally point-mutation alone might not be sufficient for the particular binary mapping in the cGA. We take up the idea from Evolution Strategy to mutate via addition of normally distributed random numbers and construct the point-mutation operator in a way to resemble this on average. This concrete approach for genetic algorithms is accompanied by more general remarks on the analysis of evolutionary algorithms in the first and the last section of this paper.