Analyzing Mutation Schemes for Real-Parameter Genetic Algorithms

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 in a binary string representation of variables. Although RGAs were first suggested in early nineties, the mutation operator is still implemented variablewise and independently for each variable. In this paper, we investigate the effect of five different mutation schemes for RGAs for two different 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 the reason for their superior working. This study signifies that the longsuggested mutation clock operator should be considered as a valuable mutation operator for RGAs.