Improving Mutation Capabilities in a Real-Coded Genetic Algorithm

This paper introduces a new method of performing mutation in a real-coded Genetic Algorithm (GA), a method driven by Principal Component Analysis (PCA). We present both theoretical and empirical results which show that our mutation operator attains higher levels of diversity in the search space, as compared to other mutation operators, meaning that by employing our mutation operator we maintain diverse populations that increase the chances of finding better solutions during evolution of the GA. The performances of the real-coded GA with PCAmutation were checked on the problem of designing IIR filters by Deczky method, which is a well known direct design method of IIR filters. Results obtained show that our PCA-mutation GA has been more successful in keeping diverse populations during search, the consequence being the finding of better solutions to the filter design problem, compared to solutions found using GA with classical mutation operators.