文献类型：会议论文

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]E China Univ Sci & Technol, Key Lab Adv Control & Optimizat Chem Proc, State Key Lab Chem Engn, Minist Educ,Automat Inst, Shanghai 200237, Peoples R China

会议论文集：7th Asian Control Conference (ASCC 2009)

会议日期：AUG 27-29, 2009

会议地点：Hong Kong, PEOPLES R CHINA

语种：英文

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, UMDA(C), EDA(G), H-EDA.