| An adaptive genetic algorithm with diversity-guided mutation and its global convergence property |
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| 引用本文： | 李枚毅,蔡自兴,孙国荣. An adaptive genetic algorithm with diversity-guided mutation and its global convergence property[J]. 中南工业大学学报(英文版), 2004, 11(3): 323-327. DOI: 10.1007/s11771-004-0066-6 |
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| 作者姓名： | 李枚毅 蔡自兴 孙国荣 |
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| 作者单位： | CollegeofInformationScienceandEngineering,CentralSouthUniversity,Changsha410083,China |
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| 摘 要： | An adaptive genetic algorithm with diversity-guided mutation, which combines adaptive probabilities of crossover and mutation was proposed. By means of homogeneous finite Markov chains, it is proved that adaptive genetic algorithm with diversity-guided mutation and genetic algorithm with diversity-guided mutation converge to the global optimum if they maintain the best solutions, and the convergence of adaptive genetic algorithms with adaptive probabilities of crossover and mutation was studied. The performances of the above algorithms in optimizing several unimodal and multimodal functions were compared. The results show that for multimodal functions the average convergence generation of the adaptive genetic algorithm with diversity-guided mutation is about 900 less than that of adaptive genetic algorithm with adaptive probabilities and genetic algorithm with diversity-guided mutation, and the adaptive genetic algorithm with diversity-guided mutation does not lead to premature convergence. It is also shown that the better balance between overcoming premature convergence and quickening convergence speed can be gotten.
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| 关 键 词： | 遗传算法 变元 马尔可夫链 收敛 |
| 收稿时间： | 2003-09-08 |
| 修稿时间： | 2003-12-12 |
An adaptive genetic algorithm with diversity-guided mutation and its global convergence property |
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| Li Mei-yi , Cai Zi-xing and Sun Guo-yun. An adaptive genetic algorithm with diversity-guided mutation and its global convergence property[J]. Journal of Central South University of Technology, 2004, 11(3): 323-327. DOI: 10.1007/s11771-004-0066-6 |
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| Authors: | Li Mei-yi Cai Zi-xing Sun Guo-yun |
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| Affiliation: | (1) College of Information Science and Engineering, Central South University, 410083 Changsha, China |
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| Abstract: | An adaptive genetic algorithm with diversity-guided mutation, which combines adaptive probabilities of crossover and mutation was proposed. By means of homogeneous finite Markov chains, it is proved that adaptive genetic algorithm with diversity-guided mutation and genetic algorithm with diversity-guided mutation converge to the global optimum if they maintain the best solutions, and the convergence of adaptive genetic algorithms with adaptive probabilities of crossover and mutation was studied. The performances of the above algorithms in optimizing several unimodal and multimodal functions were compared. The results show that for multimodal functions the average convergence generation of the adaptive genetic algorithm with diversity-guided mutation is about 900 less than that of adaptive genetic algorithm with adaptive probabilities and genetic algorithm with diversity-guided mutation, and the adaptive genetic algorithm with diversity-guided mutation does not lead to premature convergence. It is also shown that the better balance between overcoming premature convergence and quickening convergence speed can be gotten. |
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| Keywords: | diversity-guided mutation adaptive genetic algorithm Markov chain global convergence |
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