Cluster-based Adaptive Mutation Mechanism To Improve the Performance of Genetic Algorithm

This paper discusses the improvement of premature convergence in genetic algorithm (GA) used for optimizing multimodal numerical problems. Mutation is the principle operation in GA for enhancing the degree of population diversity, but is not efficient often, particularly in traditional GA. Moreover, the definition of mutation rate is a tradeoff between computing time and accuracy. In our work, we introduce the cluster method nearest neighborhood for estimating population diversity. According to this estimation, the mutation rate is adaptively given and repeat chromosomes are discarded over evolution. Consequently, the proposed cluster-based GA can choose a suitable mutation number for reducing computing time and maintain the population variety for preventing premature convergence. It is confirmed in numerical optimization simulations that the proposed GA is superior than traditional GA used fixed mutation rate in terms of accuracy, computing time and convergent speed