Fuzzy adaptive genetic algorithm based on auto-regulating fuzzy rules

Abstract

There are defects such as the low convergence rate and premature phenomenon on the performance of simple genetic algorithms (SGA) as the values of crossover probability (P_c) and mutation probability (P_m) are fixed. To solve the problems, the fuzzy control method and the genetic algorithms were systematically integrated to create a kind of improved fuzzy adaptive genetic algorithm (FAGA) based on the auto-regulating fuzzy rules (ARFR-FAGA). By using the fuzzy control method, the values of P_c and P_m were adjusted according to the evolutional process, and the fuzzy rules were optimized by another genetic algorithm. Experimental results in solving the function optimization problems demonstrate that the convergence rate and solution quality of ARFR-FAGA exceed those of SGA, AGA and fuzzy adaptive genetic algorithm based on expertise (EFAGA) obviously in the global search.

Keywords

adaptive genetic algorithm / fuzzy rules / auto-regulating / crossover probability adjustment

References

[1] ChenG.-l., WangX.-f., ZhuangZ.-quan.Genetic algorithms and applications [M], 1999, Beijing, Posts and Telecom Press

[2] FangL., ZhangH.-c., JiangY.-zhi.. A new fuzzy adaptive genetic algorithm [J]. Journal of Electronic Science and Technology of China, 2005, 3(1): 57-59, 71

[3] LiS.-g., WuZ.-m., PangX.-hong.. A fuzzy rule based genetic algorithm and its application in FMS [J]. High Technology Letters, 2005, 11(1): 56-60

[4] VasconcelosJ. A., RamrezJ. A., TakahashiR. H. C., SaidanhaR. R.. Improvements in genetic algorithms [J]. IEEE Transactions on Magnetics, 2001, 37(5): 3414-3417

[5] LuoJ.-x., ShaoH.-he.. A neurofuzzy system based on rough set theory and genetic algorithm [J]. Journal of Harbin Institute of Technology: New Series, 2005, 12(3): 278-282

[6] WangX., YangC.-h., QinB., GuiW.-hua.. Parameter selection of support vector regression based on hybrid optimization algorithm and its application [J]. Journal of Control Theory and Applications, 2005, 3(4): 371-376

[7] SrinvivasM., PatnikL. M.. Adaptive probabilities of crossover and mutation in genetic algorithms [J]. IEEE Transactions on Systems, Man and Cybernetics, 1994, 24(4): 656-667

[8] XiP.-y., WangB., ShentuL.-f., HuH.-yin.. Fuzzy optimization of an elevator mechanism applying the genetic algorithm and neural networks [J]. International Journal of Plant Engineering and Management, 2005, 10(10): 236-240

[9] TongX.-h., WuS.-c., WuS.-q., LiuD.-jie.. A novel vehicle navigation map matching algorithm based on fuzzy logic and its application [J]. Journal of Central South University of Technology, 2005, 12(2): 214-219

[10] LiuB., WangY., WangH.-jian.. Using genetic algorithm based fuzzy adaptive resonance theory for clustering analysis [J]. Journal of Harbin Engineering University, 2006, 27(S): 547-551

[11] ParmodK., ChandnaV. K., ThomasM. S.. Fuzzy-genetic algorithm for pre-processing data at the RTU [J]. IEEE Transactions on Power Systems, 2004, 19(2): 718-723

[12] WANG Ke-jun. A new fuzzy genetic algorithm based on population diversity [C]// Proceedings of 2001 IEEE International Symposium on Computational Intelligence in Robotics and Automation. Banff, Alberta, Canada, 2001: 108–112.

[13] LI Fa-chao, SU Lian-qing, RAN Hai-chao. The fuzzy genetic algorithm based on rule [C]// Proceeding of Fourth International Conference on Machine Learning and Cybernetics. Guangzhou, 2005: 2454–2459.

[14] LiuX.-c., YuS.-yi.. A genetic algorithm with fast local adjustment [J]. Chinese Journal of Computers, 2006, 29(1): 100-105

[15] GuoG.-qi.Analysis and suppressing methods of genetic drift in evolutionary computation [D], 2003, Changsha, School of Information Science and Engineering, Central South University: 93-95

[16] LiM.The study on improved genetic algorithm and its application in optimization questions [D], 2004, Changchun, School of Mathematics, Jilin University: 17