文献类型：期刊文献

英文题名：Evolutionary algorithm using kernel density estimation model in continuous domain

作者：Luo, Na[1]; Qian, Feng[1]

机构：[1] Automatic Institute, State-Key Laboratory of Chemical Engineering, East China University of Science and Technology, Shanghai, 200237, China

年份：2009

起止页码：1526

外文期刊名：Proceedings of 2009 7th Asian Control Conference, ASCC 2009

收录：EI(收录号：20095012545828)

语种：英文

外文关键词：Probability distributions - Statistics

摘要：Estimation of Distribution Algorithm (EDA) is a kind of evolutionary algorithm which updates and samples from probabilistic model in evolutionary course. The key of EDA is the construction of probability model suitable for real distribution. Gaussian distribution is widely used in EDAs but the assumption of normality is not realistic for many real-life problems. In this paper, a new EDA using kernel density estimation (KEDA) is introduced. Adaptive change strategy of kernel width is presented and selection scheme, sampling method are also given cooperated with KEDA. The results of 5 benchmark functions show that results of KEDA outperform PBILC, UMDAC, EDAG, H-EDA. ?2009 ACA.