| 摘要: |
| 在PBIL(population base dincremental learning)算法和自私基因算法的基础上,提出一个新的优化搜索算法——基因学习算法.该算法允许每个等位基因取多值(复等位基因),并且用信息熵作为结束条件的判据.在学习过程中还与局部启发式搜索法相结合.最后用基因学习算法解决了3个典型的组合优化问题(最大截问题、调度问题和旅行商问题),取得了比现有文献最优值还好的结果. |
| 关键词: 进化计算 遗传算法 局部搜索 概率模型 基因学习算法 |
| DOI： |
| 分类号: |
| 基金项目:浙江省教委基金资助项目(961100) |
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| A Learning Algorithm for Optimum Search |
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JIN Bing-yao,WEI Cheng-jian,HE Zhen-ya
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| Abstract: |
| In this paper, a new gene learning algorithm for optimum search problem is proposed, which extended the binary population-based incremental learning (PBIL) and selfish algorithm (SA) by allowing a gene's allele to be multi-valued. In this new algorithm, the entropy of probability distribution as used as the criterion of termination, and the evolution process is combined with local heuristic search. Three typical combinatorial optimization problems (maximum cut problem, scheduling problem and travelling salesman problem) are solved and some results are better than the best result of existing algorithm. |
| Key words: evolutionary computation genetic algorithm local search probabilistic modeling gene learning algorithm (GL) |
