Evolutionary Continuous Optimization by Bayesian Networks and Guassian Mixture Model

摘要：

In this paper, an evolutionary continuous optimization algorithm based on Bayesian networks and Gaussian mixture model (GMM) is proposed. A Bayesian network is used to model the relationship of variables in individual vector and the learned graphical structure is decomposed into subgraphs representing subproblems. Subsequently, GMM is adopted to model the probability distribution of each subproblem and its parameters are estimated by the expectation-maximization (EM) algorithm. New samples are generated from the GMM of each subproblem and finally are mixed into new individuals. It is demonstrated by numerical examples that the proposed algorithm could achieve better performance than previous related algorithms.

关键词： evolutionary continuous optimization Bayesian networks Gaussian mixture model

作者: Xin Wei

作者单位: Department of Information Science & Electronic Engineering Zhejiang University Hangzhou, China

会议类型: 国际会议

会议名称: 2010 IEEE 10th International Conference on Signal Processing(第十届信号处理国际会议 ICSP 2010)

会议地点: 北京

会议语种:英文

页码: 1437-1440

在线出版日期: 2010-08-24（万方平台首次上网日期，不代表论文的发表时间）