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一种基于混合因子分析的分布估计算法

Estimation of Distribution Algorithms Based on Mixtures of Factor Analyzers
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摘要 提出了一种基于混合因子分析的分布估计算法.首先用次胜者受罚的竞争学习算法对选出的最优个体集合聚类,然后对每个类用因子分析模型进行分布信息的估计.为了保持种群的多样性,算法保留那些具有较好适应值并且与所选的最优个体集合较远的个体,并利用聚类的参数来减少计算量.试验结果证实了算法的性能. The estimation of distribution algorithms （EDAs） based on mixtures of factor analyzers is proposed. The selected optimal individuals are clustered with rival penalized competitive learning algorithm, and for each single cluster, the factor analyzer model is used to estimate its distribution information. In order to maintain the population diversity, the algorithm retains the individuals which have better fitness and farther away from the sets of the selected individuals, and uses the parameters of clustering to reduce computation cost. Experimental result approves the performance of the algorithm.
作者 杨晔宏 李伟生 李翠霞
机构地区 北京交通大学计算机与信息技术学院
出处 《信息与控制》 CSCD 北大核心 2006年第4期448-452,共5页 Information and Control
关键词 进化计算 聚类 分布估计算法 因子分析 evolutionary computation clustering estimation of distribution algorithm factor analysis
分类号 TP301 [自动化与计算机技术—计算机系统结构]
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参考文献16

  • 1Zhang Q F, Mublenbein H. On the convergence of a class of optimization algorithms using estimation of distribution [ J ]. IEEE Transactions on Evolutionary Computation, 2004, 8 ( 2 ) : 127-136.
  • 2Baluja S. Population-based Incremental Learning: a Method for Integrating Genetic Searching Based Function Optimization and Competing Learning [ R ]. Pittsburgh: Carnegie Mellon University, 1994.
  • 3De Bonet J S, Isbell J S Jr, Viola P. MIMIC: finding optima by estimating probability densities [ A ]. Advances in Neural Information Processing Systems 9 [ M ]. Cambridge, MA, USA : MIT Press, 1997. 424-430.
  • 4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
  • 5Chickering D M. Learning Bayesian networks is NP-complete[ A]. Learning from Data: Artificial Intelligence and Statistics V[M]. New York, USA: Springer, 1996. 121-130.
  • 6Muhlenbein H, Mahnig T. FDA - a scalable evolutionary algorithm for the optimization of additively decomposed functions [ J].Evolutionary Computation, 1999, 7(4) : 353 - 376.
  • 7Zhang Q F. On stability of fixed points of limit models of univariate marginal distribution algorithm and factorized distribution algorithm [ J ]. IEEE Transactions on Evolutionary Computation,2004, 8(1): 80-93.
  • 8Cho D Y, Zhang B T. Continuous estimation of distribution algorithms with probabilistic principal component analysis [ A ]. Proceedings of the IEEE Conference on Evolutionary Computation[C]. Piscataway, NJ, USA: IEEE, 2001. 521 -526.
  • 9Tipping M E, Bishop C M. Probabilistic principal component analysis [J]. Journal of the Royal Statistical Society: Series B,1999, 61(3): 611 ~622.
  • 10Cho D Y, Zhang B T. Evolutionary optimization by distribution estimation with mixtures of factor analyzers [ A ]. Proceedings of the 2002 Congress on Evolutionary Computation [ C ]. Piscataway, NJ, USA: IEEE, 2002. 1396 - 1401.

二级参考文献27

  • 1Baluja S.. Population-based incremental learning: A method for integrating genetic searching based function optimization and competing learning. Carnegie Mellon University, Pittsburgh, PA, USA: Technical Report CMU-CS-94-163, 1994
  • 2Muhlenbein H.. The equation for response to selection and its use for prediction. Evolutionary Computation, 1998, 5(3): 303～346
  • 3De Bonet, Isbell J.S., Viola P.. MIMIC: Finding optima by estimating probability density. In: Mozer M.C., Jordan M.I., Petsche T. eds.. Advances in Neural Information Processing System. Cambridge: The MIT Press, 1997, 424～431
  • 4Larranga P., Etxeberria R., Lozano A., Pefia J.M.. Optimization by learning and simulation of Bayesian and Gaussian networks. In: Proceedings of the 2000 Genetic and Evolutionary Computation Conference Workshop Program, Las Vegas, Nevada, USA, 2000, 201～204
  • 5Baluja S., Davies S.. Using optimal dependency-tree for combinatorial optimization: Learning the structure of the search space. Carnegie Mellon University, Pittsburgh, PA, USA: Technical Report CMU-CS-97-107, 1997
  • 6Soto M., Ochoa A., Acid S. et al.. Introduction the polytree approximation of distribution algorithm. In: Proceedings of the 2nd Symposium on Artificial Intelligent Adaptive Systems, CIMAF'99, La Habana, 1999, 360～367
  • 7Peliken M., Muhlenbein H.. The bivariate marginal distribution algorithm. In: Roy R., Furnhashi T., Chandhery P.K. eds.. Advance in Soft Computing Engineering Design and Manufacturing. London: Springer-Verlag, 1999, 521～535
  • 8Muhlenbein H., Mahnig T.. FDA-A scalable evolutionary algorithm for the optimization of additively decomposed function. Evolutionary Computation, 1999, 7(4): 353～376
  • 9Pelikan M.,Goldberg D.E.,Cantu-Paz E..BOA:The Bayesian optimization algorithm.In: Proceedings of the Genetic and Evolutionary Computation Conference, Orlando, Florida, USA, 1999, 525～532
  • 10Zhang B.T.. A Bayesian framework for evolutionary computation. In: Proceedings of the 1999 Congress on Evolutionary Computation, 1999, 1: 722～728

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信息与控制
2006年 第4期

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