Genotype Reuse More Important than Genotype Size in Evolvability of Embodied Neural Networks

The relative influence of genotype reuse and number of genotype parameters on the evolvability of an embodied neural network is explored. Two genotype to phenotype mappings are used to encode a neural network controlling a hexapod agent. A symmetric encoding reuses the genotype by duplicating parts of the genotype to create the phenotype. A direct encoding maps one genotype parameter to one phenotype parameter. To test whether genotype reuse is more important than genotype size, the architecture of the neural network is manipulated such that the genotype size of the symmetrically-encoded neural networks is larger than the directly-encoded neural networks. The symmetrically-encoded neural networks are found to be more evolvable than the directly-encoded despite having a larger genotype.