Selection in Massively Parallel Genetic Algorithms.

Abstract

The availability of massively parallel computers makes it possible to apply genetic algorithms to large populations and very complex applications. Among these applications are studies of natural evolution in the emerging field of artificial life, which place special demands on the genetic algorithm. In this paper, we characterize the difference between panmictic and local selection/mating schemes in terms of diversity of alleles, diversity of genotypes, the inbreeding coefficient, and the speed and robustness of the genetic algorithm. Based on these metrics, local mating appears to not only be superior to panmictic for artificial evolutionary simulations, but also for more traditional applications of genetic algorithms.