Tuning Genetic Algorithms for Problems Including Neutral Networks

Neutral networks, which occur in fitness landscapes containing neighboring points of equal fitness, have attracted much research interest in recent years. In this work, we applied a standard GA and an extended GA with a variable mutation rate strategy to an abstract model of neutral networks in order to investigate the effects of selection pressure and mutation rate on the speed of population movement. Our results demonstrate that speed has an optimal mutation rate and an error threshold since plotting speed against mutation rate results in a concave curve. Increasing selection pressure increased the speed of a population’s movement on a neutral network. The variable mutation rate strategy of the extended GA improved the efficiency of the search. For both GAs, we found that high selection pressure was preferable, both for increasing the speed of population movement and for avoiding the effects of an error threshold on a neutral network.