The neutral theory of evolution suggests that most mutations do not cause a phenotypic change. In this case the mapping from genotype to phenotype contains redundancy such that many mutations do not have an appreciable effect on the phenotype. This can result in neutral networks; sets of genotypes connected by single point mutations that map to the same phenotype. A population is able to drift along these networks, eventually encountering phenotypes of higher fitness, thus reducing the chance of becoming trapped in sub-optimal regions of genotype space. In this paper we explore the use and benefit of redundant mappings for evolutionary search. We investigate the properties of several genotype-phenotype mappings by performing random walks along the neutral networks in their genotype spaces. The properties are explored further by performing adaptive walks in which a concept of fitness is introduced. A mapping based on a random Boolean network was found to have particularly interesting properties in both cases.

Evolutionary algorithms apply the processes of variation, reproduction and selection to look for an individual that is capable of solving the task in hand. In order to improve the evolvability of a population, we propose to copy important characteristics of nature's search space. Desired characteristics for a genotype-phenotype mapping are described, and several highly redundant genotype-phenotype mappings are analyzed in the context of a population-based search. We show that evolvability is influenced by the existence of neutral networks in the genotype space. The extent of the neutral networks affects the interconnectivity of the search space and thereby affects evolvability. Species evolving on a non-redundant mapping reach a state of stasis after a few generations; in effect, evolution comes to a halt. However, species evolving on a genotype-phenotype mapping with extensive neutral networks are continuously able to find adaptive mutations and are able to locate higher optima. The existence of highly intertwined neutral networks increases the evolvability of a population.