Diversifying Techniques & Neutrality in Genetic Algorithms

This paper examines the implicit maintenance of diversity w ithin a population through the inclusion of a layered genotype-phenotype map (GP-map) in a Genetic Algor ithm (GA), based on the principal of Neutral theory (Kimura, 1968). The paper compares a simple GA (SGA), incorporating a variety of diversifying techniques, to the multi-layered GA (MGA) as proposed by the auth ors. The MGA creates a neutral representation by including a layered GP-map based on the biological concep ts ofTranscriptionandTranslation. In standard GAs, each phenotype is represented by a distinct genotype. H owever by allowing a higher number of alleles to encode phenotypic information on the genotype, one can crea te a situation where a number of genotypes may represent the same phenotype. Through this process one can i ntroduce the idea of redundancy or neutrality into the representation. This representation allows for ad aptive mutation (hot spots) and silent mutation (cold spots). This combination enables the level of diversity to d ynamically adjust during the search, and directs the search towards closely related neutral sets. Previous work has shown that introducing this type of representation can be beneficial; in this paper we show how this represen tatio is useful at introducing and maintaining diversity. Here we compare the performance of the MGA agains t traditional diversifying techniques used in conjunction with a SGA over a fully deceptive changing lands cape.