Neutral search spaces for artificial evolution: a lesson from life

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4 Author(s)

Natural evolutionary systems exhibit a complex mapping from genotype to phenotype. One property of these mappings is neutrality, where many mutations do not have an appreciable effect on the phenotype. In this case the mapping from genotype to phenotype contains redundancy such that a phenotype is represented by many genotypes. Studies of RNA and protein molecules, the fundamental building blocks of life, reveal that this can result in neutral networks - sets of genotypes connected by single point mutations that map into the same phenotype. This allows genetic changes to be made while maintaining the current phenotype and thus may reduce the chance of becoming trapped in sub-optimal regions of genotype space. In this paper we present several redundant mappings and explore their properties by performing random walks on the neutral networks in their genotype spaces. We investigate whether the properties found in nature's search space can he engineered into our artificial evolutionary systems. A mapping based on a random hoolean network was found to give particularly promising results