Optimization using distributed genetic algorithms

Abstract

A distributed genetic algorithm is tested on several difficult optimization problems using a variety of different subpopulation sizes. Contrary to our previous results, the more comprehensive tests presented in this paper show the distributed genetic algorithm is often, but not always superior to genetic algorithms using a single large population when the total number of evaluations is held constant. Data collected concerning execution times show that the GENITOR genetic algorithm using multiple subpopulations may execute much faster than the single population version when the cost of the evaluation function is low; thus, total number of evaluations is not always a good metric for making performance comparisons. Finally, our results suggest that "adaptive mutation" may be an important factor in obtaining superior results using a distributed version of GENITOR.