Evolutionary Dynamics of Genetic Algorithms on Fitness Landscapes with Neutral Networks

A Case Study of Evolutionary Robotics Problems

Abstract

Neutral networks, which occur in fitness landscapes containing neighboring points of equal fitness, have attracted much research interest in recent years. In our recent papers, 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 increase as the ruggedness of the landscapes increases. In this paper, we conducted a series of computer simulations with evolutionary robotics (ER) problems in order to investigate whether our previous results are applicable to this problem domain. 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 the ER problems.