{"id":2124,"date":"2010-12-17T16:33:03","date_gmt":"2010-12-17T16:33:03","guid":{"rendered":"http:\/\/hgpu.org\/?p=2124"},"modified":"2010-12-17T16:33:03","modified_gmt":"2010-12-17T16:33:03","slug":"gpu-accelerated-differential-evolutionary-markov-chain-monte-carlo-method-for-multi-objective-optimization-over-continuous-space","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=2124","title":{"rendered":"GPU-accelerated differential evolutionary Markov Chain Monte Carlo method for multi-objective optimization over continuous space"},"content":{"rendered":"<p>In this paper, the attractive features of evolutionary algorithm and Markov Chain Monte Carlo are combined into a new Differential Evolutionary Markov Chain Monte Carlo (DE-MCMC) method for multi-objective optimization problems with continuous variables. The DE-MCMC evolves a population of Markov chains through differential evolution (DE) toward a diversified set of solutions at the Pareto optimal front in the multi-objective function space. The computational results show the effectiveness of the DE-MCMC algorithm in a variety of standardized test functions as well as a protein loop structure sampling application. Moreover, the DE-MCMC algorithm can efficiently take advantage of the massive-parallel, many-core architecture, where its implementation on GPU can achieve speedup of 14~35.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>In this paper, the attractive features of evolutionary algorithm and Markov Chain Monte Carlo are combined into a new Differential Evolutionary Markov Chain Monte Carlo (DE-MCMC) method for multi-objective optimization problems with continuous variables. The DE-MCMC evolves a population of Markov chains through differential evolution (DE) toward a diversified set of solutions at the Pareto [&hellip;]<\/p>\n","protected":false},"author":351,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":false,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[157,3],"tags":[1796,20,234,298],"class_list":["post-2124","post","type-post","status-publish","format-standard","hentry","category-mathematics","category-paper","tag-mathematics","tag-nvidia","tag-nvidia-geforce-gtx-280","tag-optimization"],"views":2211,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/2124","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/users\/351"}],"replies":[{"embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=2124"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/2124\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=2124"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=2124"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=2124"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}