An Approach to Evolutionary Robotics Using a Genetic Algorithm with a Variable Mutation Rate Strategy

Abstract

Neutral networks, which occur in fitness landscapes containing neighboring points of equal fitness, have attracted much research interest in recent years. In recent papers [20,21], we have shown that, in the case of simple test functions, the mutation rate of a genetic algorithm is an important factor for improving the speed at which a population moves along a neutral network. Our results also suggested that the benefits of the variable mutation rate strategy used by the operon-GA [5] increase as the ruggedness of the landscapes increases. In this work, we conducted a series of computer simulations with an evolutionary robotics problem in order to investigate whether our previous results are applicable to this problem domain. Two types of GA were used. One was the standard GA, where the mutation rate is constant, and the other was the operon-GA, whose effective mutation rate at each locus changes independently according to the history of the genetic search. The evolutionary dynamics we observed were consistent with those observed in our previous experiments, confirming that the variable mutation rate strategy is also beneficial to this problem.