Comparison and Analysis of Different Mutation Strategies to improve the Performance of Genetic Algorithm

Genetic algorithm (GA), as an important intelligence computing tool, is a wide research content in the application domain and the academic circle now. This paper elaborates the improvement of premature convergence in GA used for optimizing multimodal numerical problems. Mutation is the principle operation in Genetic Algorithm (GA) for enhancing the degree of population diversity, but it is proved that it is not efficient often, mostly in traditional GA. The mutation rate is a tradeoff between computing time and accuracy. This paper presents a comparative analysis of different mutation approaches, based on the distributions for the purpose to examine their performance, evaluate the average improvement of chromosomes and investigate their ability to find solutions with the high precision. The proposed approach consists of mainly three components. The first component describes simple genetic algorithm with the problem of genetic algorithm. The second component elaborates on different mutation strategies of genetic algorithm. These strategies improves the performance of genetic algorithm which promotes and enriches the existing intelligent optimization theory and methods, and have a wide application prospect in optimization of complex systems, production management and other fields. In this paper, we have compared Dynamic Mutation Genetic Algorithm (DMGA), Schema Mutation Genetic Algorithm(SM-GA) algorithm, Compound Mutation (BCM-GA) algorithm, Clustered based adaptive mutation (CBAM) algorithm, Hyper Mutation Based Dynamic Algorithm (HMDA).