Local evolvability of statistically neutral GasNet robot controllers

In this paper we introduce and apply the concept of local evolvability to investigate the behaviour of populations during evolutionary search. We focus on the evolution of GasNet neural network controllers for a robotic visual discrimination problem, showing that the evolutionary process undergoes long neutral fitness epochs. We show that the local evolvability properties of the search space surrounding a group of statistically neutral solutions do vary across the course of an evolutionary run, especially during periods of population takeover. However, once takeover is complete there is no evidence for further increase in local evolvability across fitness epochs. We also see no evidence for the neutral evolution of increased solution robustness, but show that this may be due to the ability of evolutionary algorithms to focus search on volumes of the fitness landscape with above average robustness.