A genetic Gaussian process regression model based on memetic algorithm

作     者：张乐 刘忠 张建强 任雄伟 

作者机构：College of ElectronicNaval University of Engineering Wuhan Mechanical Technology College 

出 版 物：《Journal of Central South University》 (中南大学学报（英文版）)

年 卷 期：2013年第20卷第11期

页      面：3085-3093页

核心收录：

学科分类：12[管理学] 02[经济学] 07[理学] 08[工学] 070103[理学-概率论与数理统计] 0202[经济学-应用经济学] 020208[经济学-统计学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 0835[工学-软件工程] 0714[理学-统计学（可授理学、经济学学位）] 0811[工学-控制科学与工程] 0701[理学-数学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Project(513300303)supported by the General Armament Department China 

主　　题：Gaussian process hyper-parameters optimization memetic algorithm regression model 

摘      要：Gaussian process(GP)has fewer parameters,simple model and output of probabilistic sense,when compared with the methods such as support vector *** of the hyper-parameters is critical to the performance of Gaussian process ***,the common-used algorithm has the disadvantages of difficult determination of iteration steps,over-dependence of optimization effect on initial values,and easily falling into local *** solve this problem,a method combining the Gaussian process with memetic algorithm was *** on this method,memetic algorithm was used to search the optimal hyper parameters of Gaussian process regression(GPR)model in the training process and form MA-GPR algorithms,and then the model was used to predict and test the *** used in the marine long-range precision strike system(LPSS)battle effectiveness evaluation,the proposed MA-GPR model significantly improved the prediction accuracy,compared with the conjugate gradient method and the genetic algorithm optimization process.