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On the Evolution of Continuous Time Recurrent Neural Networks with Neutrality

The evolution of continuous time recurrent neural networks is increasingly being employed to evolve nervous systems for autonomous agents. Nonetheless, the picture of populations engaged in hill-climbing rugged fitness landscapes poses a problem of becoming trapped on a local hilltop. Recent development in evolutionary theory and molecular biology points to the importance of selective neutrality. However, neutral networks are only known to work in binary (or discrete alphabet) genotypes. This work hypothesizes and provides evidence that neutrality can be introduced in the dynamics of evolving CTRNNs by (a) changing the encoding scheme to a binary connectivity matrix genotype or (b) by changing the genotypephenotype process to a redundant neuron-expressing mapping. Conclusions are made for future work in this area to point towards more developmental mappings.