| Combinations of Estimation of Distribution Algorithms and Other Techniques |
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| 作者姓名： | Qingfu Zhang Jianyong Sun Edward Tsang |
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| 作者单位： | Department of |
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| 摘 要： | This paper summaries our recent work on combining estimation of distribution algorithms (EDA) and other techniques for solving hard search and optimization problems:a) guided mutation,an offspring generator in which the ideas from EDAs and genetic algorithms are combined together,we have shown that an evolutionary algorithm with guided mutation outperforms the best GA for the maximum clique problem,b)evolutionary algorithms refining a heuristic,we advocate a strategy for solving a hard optimization problem with complicated data structure,and c) combination of two different local search techniques and EDA for numerical global optimization problems,its basic idea is that not all the new generated points are needed to be improved by an expensive local search.
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| 关 键 词： | 分布算法 整体优化系统 运算法则 计算机技术 |
| 收稿时间： | 5 March 2007 |
| 修稿时间： | 2007-03-052007-05-23 |
Combinations of estimation of distribution algorithms and other techniques |
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| Qingfu Zhang Jianyong Sun Edward Tsang.Combinations of Estimation of Distribution Algorithms and Other Techniques[J].International Journal of Automation and computing,2007,4(3):273-280. |
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| Authors: | Qingfu Zhang Jianyong Sun Edward Tsang |
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| Affiliation: | (1) Department of Computer Science, University of Essex, Wivenhoe Park, Colchester, CO4 3SQ, UK;(2) School of Computer Science, University of Birmingham, Birmingham, B15 2TT, UK |
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| Abstract: | This paper summaries our recent work on combining estimation of distribution algorithms (EDA) and other techniques for solving hard search and optimization problems:a) guided mutation,an offspring generator in which the ideas from EDAs and genetic algorithms are combined together,we have shown that an evolutionary algorithm with guided mutation outperforms the best GA for the maximum clique problem,b)evolutionary algorithms refining a heuristic,we advocate a strategy for solving a hard optimization problem with complicated data structure,and c) combination of two different local search techniques and EDA for numerical global optimization problems,its basic idea is that not all the new generated points are needed to be improved by an expensive local search. |
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| Keywords: | Estimation distribution algorithm guided mutation memetic algorithms global optimization |
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