Diversifying techniques & neutrality in genetic algorithms

This paper examines the implicit maintenance of diversity within a population through the inclusion of a layered genotype-phenotype map (GP-map) in a Genetic Algorithm (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 authors. The MGA creates a neutral representation by including a layered GP-map based on the biological concepts of Transcription and Translation. In standard GAs, each phenotype is represented by a distinct genotype. However by allowing a higher number of alleles to encode phenotypic information on the genotype, one can create a situation where a number of genotypes may represent the same phenotype. Through this process one can introduce the idea of redundancy or neutrality into the representation. This representation allows for adaptive mutation (hot spots) and silent mutation (cold spots). This combination enables the level of diversity to dynamically 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 representation is useful at introducing and maintaining diversity. Here we compare the performance of the MGA against traditional diversifying techniques used in conjunction with a SGA over a fully deceptive changing landscape.