Large-Scale Estimation of Distribution Algorithms with Adaptive Heavy Tailed Random Pro jection Ensembles  

作　　者：Momodou L.Sanyang Ata Kabán 

机构地区：[1]School of Computer Science,University of Birmingham,Edgbaston,Birmingham B152TT,U.K. [2]School of Information Technology and Communication,University of The Gambia,Serekunda,The Gambia

出　　处：《Journal of Computer Science & Technology》2019年第6期1241-1257,共17页计算机科学技术学报（英文版）

基　　金：partly funded by a Ph.D.scholarship from the Islamic Development Bank;funded by the Engineering and Physical Sciences Research Council of UK under Fellowship Grant EP/P004245/1.

摘　　要：We present new variants of Estimation of Distribution Algorithms (EDA) for large-scale continuous optimisation that extend and enhance a recently proposed random projection (RP) ensemble based approach. The main novelty here is to depart from the theory of RPs that require (sub-)Gaussian random matrices for norm-preservation, and instead for the purposes of high-dimensional search we propose to employ random matrices with independent and identically distributed entries drawn from a t-distribution. We analytically show that the implicitly resulting high-dimensional covariance of the search distribution is enlarged as a result. Moreover, the extent of this enlargement is controlled by a single parameter, the degree of freedom. For this reason, in the context of optimisation, such heavy tailed random matrices turn out to be preferable over the previously employed (sub-)Gaussians. Based on this observation, we then propose novel covariance adaptation schemes that are able to adapt the degree of freedom parameter during the search, and give rise to a flexible approach to balance exploration versus exploitation. We perform a thorough experimental study on high-dimensional benchmark functions, and provide statistical analyses that demonstrate the state-of-the-art performance of our approach when compared with existing alternatives in problems with 1000 search variables.

关 键 词：covariance adaptation estimation of distribution algorithm random projection ensemble T-DISTRIBUTION 

分 类 号：O15[理学—数学]