Evolving dynamical neural networks for adaptive behavior
Adaptive Behavior
Automatic definition of modular neural networks
Adaptive Behavior
Bias and scalability in evolutionary development
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Measuring, enabling and comparing modularity, regularity and hierarchy in evolutionary design
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Towards an empirical measure of evolvability
GECCO '05 Proceedings of the 7th annual workshop on Genetic and evolutionary computation
Demonstrating the evolution of complex genetic representations: an evolution of artificial plants
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
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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.