Analysing mutation schemes for real-parameter genetic algorithms

Abstract

Mutation is an important operator in genetic algorithms GAs, as it ensures maintenance of diversity in evolving populations of GAs. Real-parameter GAs RGAs handle real-valued variables directly without going to a binary string representation of variables. Although RGAs were first suggested in early '90s, the mutation operator is still implemented variable-wise - in a manner that is independent to each variable. In this paper, we investigate the effect of five different mutation schemes for RGAs using two different mutation operators - polynomial and Gaussian mutation operators. Based on extensive simulation studies, it is observed that a mutation clock implementation is computationally quick and also efficient in finding a solution close to the optimum on four different problems used in this study for both mutation operators. Moreover, parametric studies with their associated parameters reveal suitable working ranges of the parameters. Interestingly, both mutation operators with their respective optimal parameter settings are found to possess a similar inherent probability of offspring creation, a matter that is believed to be the reason for their superior working. This study signifies that the long suggested mutation clock operator should be considered as a valuable mutation operator for RGAs.