Altering the granularity of neutrality in a multi-layered genetic algorithm

Abstract

By adopting a basic interpretation of the biological processes of transcription and translation, the multilayered GA (MGA) introduces a genotype-phenotype mapping for a haploid genotype, which allows the granularity of the representation to be tuned. The paper examines the impact of altering the level of neutrality through changes in the granularity of the representation and compares the performance of a standard GA (SGA) to that of a number of multi-layered GAs, each with a different level of neutrality, over both static and changing environments. Initial results indicate that it appears advantageous to include a multi-layered, biologically motivated genotype-phenotype encoding over more difficult landscapes. The paper also introduces an interpretation of missense mutation, which operates within the genotype-phenotype map (GP-map). Results also suggest that this mutation strategy can assist in tracking the optimum over various landscapes.