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.