一种基于混合因子分析的分布估计算法

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．

Estimation of Distribution Algorithms Based on Mixtures of Factor Analyzers

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.