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高性能自适应调整参数的遗传算法 被引量：2

Highly Efficient Genetic Algorithms With Adaptive Adjustment Of Parameters
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摘要 分析了适应值选择算子中参数对遗传算法的个体选择和搜索性能的影响 ,提出了一种高性能自适应调整选择算子参数的遗传算法 ,修正了交叉概率和变异概率的自适应形式 ,并讨论了它们的变化机理 .实验证明 ,该算法提高了算法搜索能力和解的精度 。 This paper analyses the influence of the parameters of fitness selection operator upon the individual selection and searching performance in genetic algorithms. Then we propose a genetic algorithms which can adaptively adjust the Parameter of selection operator, modify the adaptive form of crossover and mutation probabilities discuss their varying mechanism. At last ,through the experiments ,we have proved this new genetic algorithms improve the searching ability and the precision of solutions, and can efficiently avoid the local minimum.
作者 钟华 高协平 段艺华
机构地区 湘潭大学信息工程学院
出处 《湘潭大学自然科学学报》 CAS CSCD 2001年第4期14-18,41,共6页 Natural Science Journal of Xiangtan University
基金 国家省自然科学基金 (6 9875 0 14) 教育部骨干教师基金 (GG - 5 2 0 - 10 5 30 - 10 2 2 ) 湖南省自然科学基金(0 0JJY2 0 5 8)
关键词 遗传算法 共享度 自适应调整 群体多样性 genetic algorithms sharing degree adaptive adjustment population multiformity
分类号 TP183 [自动化与计算机技术—控制理论与控制工程]
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参考文献5

  • 1[1]Cavicchio D J. Adaptive Search Using Simulated Evolution:Doctoral Dissert ation[M].Michigan:University of Michigan,Ann,Arbor,1970
  • 2[2]Cavicchio D J. Reproductive Adaptive Plans[C].Proceedings of the ACM 19 72 Annual Conference,1972.1-11
  • 3[3]Goldberg D E.Richardson J.Genetic algorithms with sharing for multim odal function optimization[C].2nd Int. Conf. Genetic Algorithms,1987,41- 49
  • 4[4]Eshelman L J.The CHC adaptive search algorithm:How to have safe sear ch when engaging in nontraditional genetic recombination[C].Foundations of gen etic Algorithms and Classifier Systems,Morgan Kaufmann,San Mateo,CA,1991
  • 5[5]Srinivas M,Patnaik L M.Adaptive Probabilities of Crossover and Mutat ion in Genetic Algorithms[J].IEEE Trans Systems Man and Cybernetics,1994,24(4) :656-667

同被引文献14

  • 1玄光男 程润伟.遗传算法与工程优化[M].北京:清华大学出版社,2004..
  • 2Herrera F,M Lozano.Adaptation of genetic algorithm parameters based on fuzzy logic controllers[C].In:Genetic Algorithms and Soft Computing,Pysica-Verlag, 1996:95-125.
  • 3Sfinivas M,Pantaik L M.Adaptive probabilities of crossover and mutation in genetic algorithnm[J].lEEE Tram on Syst Man and Cybernetics, 1994;24(4):656-667.
  • 4Schaffer J D,Caruana R A,Eshelman L J et al.A Study of Control Parameters Affecting Online Performance of Genetic Algorithms for Function Optimization[C].In:Proc of the 3rd inter Conf on Genetic algorithms, George Mason University, United States, 1989:51-60.
  • 5Deb K,Anand A,Joshi D.A computationally efficient evolutionary algorithm for real-parameter optimization[J].Evolutionary Computation Journal ,2002;10(4):371-395.
  • 6《齿轮制造工艺手册:滚、插、磨、剃、刨》编委会.齿轮制造工艺手册:滚、插、磨、剃、刨[M].北京:机械工业出版社,2010.
  • 7ZHAN Zhihui, ZHANG Jun. Self-Adaptive Differential Evo- lution Based on PSO Learning Strategy [ J ]. GECCO, 2010 (10) :39 -46.
  • 8PRICE K V, STOMR M, LAMPINEN J. A Differential Evolu- tion:A Practical Approach to Global Optimization [ C ]. Ber- lin, Germany: Springer-Verlag,2005.
  • 9LIU J,LAMPINEN J. "A fuzzy adaptive differential evolution algorithm", Soft Comput [ J ]. A Fusion of Foundation, Meth- odologies and Applications, 2005,9 (6) :448 - 462.
  • 10MALLIPEDDI R, SUGANTHAN P N, PAN Q K, et al. Differ- ential Evolution Algorithm with Ensemble of Parameters and Mutation Strategies [ J ]. Applied Soft Computing, 2011,11 (2) :1679 - 1696.

引证文献2

二级引证文献10

湘潭大学自然科学学报
2001年 第4期

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