Simulation research based on a self-adaptive genetic algorithm
Crossover probability Pc and mutation probability Pm arc important parameters of genetic algorithm. Self-adaptive genetic algorithm can reach good balance between convergence speed and global optimum by adjusting Pc and Pm adaptively according to the fitnessivalues difference among individuals. But it is not suitable to the early period of the evolutionary process. The improved self-adaptive GA proposed by this paper can avoid this drawback. And this paper trains a neural network by using the three algorithms respectively. Simulation results show that the improved self-adaptive genetic algorithm is optimal.
self-adaptive genetic algorithm crossover probability mutation probability
Jiang Jing Meng Li-dong Li Shu-ling Jiang Lin
School of Electrical and Electronic-Engineering Shandong University of Technology Zibo, Shandong Pro The Second Steel Plant Anyang Steel and Iron Corp Anyang, Henan Province, China
国际会议
厦门
英文
267-269
2010-10-29（万方平台首次上网日期，不代表论文的发表时间）