{"id":5542,"date":"2011-09-12T14:14:44","date_gmt":"2011-09-12T11:14:44","guid":{"rendered":"http:\/\/hgpu.org\/?p=5542"},"modified":"2011-09-12T14:14:44","modified_gmt":"2011-09-12T11:14:44","slug":"implementing-cartesian-genetic-programming-classifiers-on-graphics-processing-units-using-gpu-net","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=5542","title":{"rendered":"Implementing cartesian genetic programming classifiers on graphics processing units using GPU.NET"},"content":{"rendered":"<p>This paper investigates the use of a new Graphics Processing Unit (GPU) programming tool called &#8216;GPU.NET&#8217; for implementing a Genetic Programming fitness evaluator. We find that the tool is able to help write software that accelerates fitness evaluation. For the first time, Cartesian Genetic Programming (CGP) was used with a GPU-based interpreter. With its code reuse and compact representation, implementing CGP efficiently on the GPU required several innovations. Further, we tested the system on a very large data set, and showed that CGP is also suitable for use as a classifier.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>This paper investigates the use of a new Graphics Processing Unit (GPU) programming tool called &#8216;GPU.NET&#8217; for implementing a Genetic Programming fitness evaluator. We find that the tool is able to help write software that accelerates fitness evaluation. For the first time, Cartesian Genetic Programming (CGP) was used with a GPU-based interpreter. With its code [&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":[36,11,3],"tags":[1787,117,1782,969,70],"class_list":["post-5542","post","type-post","status-publish","format-standard","hentry","category-algorithms","category-computer-science","category-paper","tag-algorithms","tag-artificial-intelligence","tag-computer-science","tag-genetic-programming","tag-programming-techniques"],"views":1962,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/5542","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=5542"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/5542\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=5542"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=5542"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=5542"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}