Adaptive Probabilities of Crossover and Mutation in Genetic Algorithms Based on Cloud Model
Traditional genetic algorithms (Gas) easily get stuck at a local optimum, and often have slow convergent speed. A novel adaptive genetic algorithm (AGA) called cloud-modelbased AGA (CAGA) is proposed in this paper. Unlike conventional genetic algorithms, CAGA presents the use of cloud model to adaptively tune the probabilities of crossover Pc and mutation pm depending on the fitness values of solutions. Because normal cloud model has the properties of randomness and stable tendency, CAGA is expected to realize the twin goals of maintaining diversity in the population and sustaining the convergence capacity of the GA. We compared the performance of the CAGA with that of the standard GA (SGA) and AGA in optimizing several typical functions with varying degrees of complexity and solving the Travelling Salesman Problems. In all cases studied, CAGA is greatly superior to SGA and AGA in terms of robustness and efficiency. The CAGA converges to the global optimum in far fewer generations, and gets stuck at a local optimum fewer times than SGA and AGA.
C.H.Dai Y.F.Zhu W.R.Chen
Institute of Electrification & Automation Southwest Jiaotong University 610031 Chengdu, China Department of Computer & communication Eng.Emei Branch, Southwest Jiaotong University 614202 Emei, School of Electrical Engineering Southwest Jiaotong University 610031 Chengdu, China
国际会议
2006年IEEE信息理论国际会议(Proceedings of 2006 IEEE Information Theory Workshop ITW06)
成都
英文
710-713
2006-10-22（万方平台首次上网日期，不代表论文的发表时间）