标题:Gaussian Process Meta-Modeling and Comparison of GP Training Methods
作者:Zhang Wenhui; Liu Xinliang
通讯作者:Zhang, WH
作者机构:[Zhang Wenhui] Shandong Univ Technol, Sch Comp Sci & Technol, Zibo 255049, Shandong, Peoples R China.; [Liu Xinliang] Natl Univ Def Technol, Sch Inf 更多
会议名称:International Conference on Logistics Systems and Intelligent Management
会议日期:JAN 09-10, 2010
来源:PROCEEDINGS OF 2010 INTERNATIONAL CONFERENCE ON LOGISTICS SYSTEMS AND INTELLIGENT MANAGEMENT, VOLS 1-3
出版年:2010
页码:1193-+
关键词:Gaussian Process Meta-modeling; Hyper-parameter Optimization; Local; Optimal Algorithm; Genetic Algorithms; Estimation of Distribution; Algorithms
摘要:The ability of Gaussian Process to flexibly and accurately fit arbitrary, even highly nonlinear data sets has lead to considerable interest in their application to many areas. Firstly, the usefulness of Gaussian Process models for application to complex systems metamodeling is proposed. Secondly, several approaches for training Gaussian Process models are examined, which include local optimization algorithm, Genetic Algorithms and Estimation of Distribution Algorithms. The results of these training methods are compared for several example problems, and guidance is provided in GP training methods.
收录类别:CPCI-S
资源类型:会议论文
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