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快速二变量边缘分布算法及其应用研究 被引量：2

Research on the Fast Algorithm of the Bivariate Marginal Distribution and it's Application
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摘要 1.引言 近年来,一些研究者从统计学的观点出发,将构造性模型引入进化算法的研究,形成一类基于概率分布的进化算法[1～3],文献中也称这类算法为分布评价算法(EDA),概率分析构造遗传算法(PMBGA)等名称,本文统一称之为概率分析进化算法,简称为PMEA(Evolutionary Algorithm basedon Probability Modeling).和传统的进化算法不同,PMEA的基本思想是通过从当前优选的解集合中提取信息,然后依据这些信息建立概率分布模型,再利用这种分布产生新的解,如此重复,直到满足算法的终止条件. This paper discusses the fast calculation problem of the bivariate marginal distribution algorithm (BMDA). A fast BMDA is proposed . An experimental case with the multi-constraints Knapsack NP-hard problem is solved using the algorithm. The results show that the algorithm has quick and accurate performance.
作者 杨小林
机构地区 湖南大学计算机系
出处 《计算机科学》 CSCD 北大核心 2002年第4期69-71,共3页 Computer Science
基金 湖南省自然科学基金
关键词 背包问题 性能分析 快速二变量边缘分布算法 遗传算法 优化算法 Evolutionary algorithm,Probabilistic model,Bivariate marginal distribution,Knapsack NP-hard problem
分类号 O224 [理学—运筹学与控制论] O242.23 [理学—计算数学]
  • 相关文献

参考文献11

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  • 2Baluia S. Fast probabilistic models for combinatorial optimization. In: Proc. of the 15th National Conf. on Artificial Intelligence (AAA-98) AAA Press, 1998. 469～ 476
  • 3Pelikan M,Muehlenbein H. Marginal distributions in evolutionary algorithms . http://www-illigal. ge. uiuc. edu/- Pelikan/
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  • 5Pelikan M. The bivariate marginal distribution algorithm . Advanced in soft computing-Engineering Design and Manufacturing,London: Springer-Verlag, 1998. 521 ～ 535
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二级参考文献43

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共引文献30

同被引文献14

  • 1钟伟才,刘静,刘芳,焦李成.二阶卡尔曼滤波分布估计算法[J].计算机学报,2004,27(9):1272-1277. 被引量：6
  • 2周树德,孙增圻.分布估计算法综述[J].自动化学报,2007,33(2):113-124. 被引量：210
  • 3Muehlenbein H.From recombination of genes to the estimation of distributions I Binary parameters[J].Parallel Problem Solving from Nature, 1996;4 : 178-187.
  • 4Baluia S.Fast probabilistic models for combinatorial optimization[C]. In:Proc of the 15 th National Conference on Artificial I Intelligence (AAA-98) ,AAA Press, 1998 :469-476.
  • 5Pelikan M.The bivariate marginal distribution algorithm,Advanced in soft computing-Engineering Design and Manufacturing[M].London: Springer-Verlag, 1998: 521-535.
  • 6Bonet J D et al.MIMIC:Finding Optima by Estimating Probability Densities,Advances in Neural Information Processing System[M].The MIT Press,Cambridge,1997.
  • 7Baluia S.Davies S.Using optimal dependency-trees for combinatorial optimization learning the structure of the search spece[C].In；Proc of the 14th International Conference on machine learning,Morgan Kaufmann,1997:30-38.
  • 8Bonet D J S,Isbell C L,Viola P.MIMIC:Finding Optima byEstimating Probability Densities[C]//Proc.of NIPS’97.Cambridge,UK:MIT Press,1997.
  • 9朱慧明,韩玉启.贝叶斯多远统计推断理论[M].北京:科学出版社,2006.
  • 10Rajkumar R P,Takeshi F P,Pravir K.Advances in SoftComputing[M].London,UK:Spring-Verlag,1999.

引证文献2

二级引证文献1

计算机科学
2002年 第4期

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