{"id":7691,"date":"2020-03-10T10:28:58","date_gmt":"2020-03-10T09:28:58","guid":{"rendered":"https:\/\/complex-systems-ai.com\/?page_id=7691"},"modified":"2022-12-03T23:03:45","modified_gmt":"2022-12-03T22:03:45","slug":"algorithme-genetique-de-tri-non-domine","status":"publish","type":"page","link":"https:\/\/complex-systems-ai.com\/en\/algorithms-devolution-2\/non-dominate-sort-genetic-algorithm\/","title":{"rendered":"Non-dominated genetic sorting algorithm"},"content":{"rendered":"<div data-elementor-type=\"wp-page\" data-elementor-id=\"7691\" class=\"elementor elementor-7691\">\n\t\t\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-523aa49 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"523aa49\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column 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class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #999;color:#999\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewbox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #999;color:#999\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewbox=\"0 0 24 24\" version=\"1.2\" baseprofile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/span><\/a><\/span><\/div>\n<nav><ul class='ez-toc-list ez-toc-list-level-1' ><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/complex-systems-ai.com\/en\/algorithms-devolution-2\/non-dominate-sort-genetic-algorithm\/#Algorithme-genetique-de-tri-non-domine-NSGA\" >NSGA non-dominated genetic sorting algorithm<\/a><\/li><\/ul><\/nav><\/div>\n<h2><span class=\"ez-toc-section\" id=\"Algorithme-genetique-de-tri-non-domine-NSGA\"><\/span>NSGA non-dominated genetic sorting algorithm<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p class=\"has-text-align-justify\">The objective of the<a href=\"https:\/\/complex-systems-ai.com\/en\/algorithms-devolution-2\/genetic-algorithms\/\">genetic algorithm<\/a> NSGA nondominated sorting is to improve the adaptive fit of a population of candidate solutions to a Pareto front constrained by a set of objective functions. The NSGA nondominated sorting genetic algorithm uses an evolutionary process with surrogates for evolutionary operators including selection, genetic crossover, and genetic mutation.<\/p>\n<p class=\"has-text-align-justify\">The population is classified into a hierarchy of subpopulations based on the order of Pareto domination. The similarity between the members of each subgroup is evaluated on the Pareto front, and the resulting groups and measures of similarity are used to promote a diverse front of non-dominated solutions.<\/p>\n\n<figure class=\"wp-block-image size-large\"><img fetchpriority=\"high\" decoding=\"async\" class=\"aligncenter wp-image-7689 size-full\" src=\"https:\/\/complex-systems-ai.com\/wp-content\/uploads\/2020\/03\/nsgaii.png\" alt=\"NSGA non-dominated genetic sorting algorithm\" width=\"463\" height=\"681\" title=\"\" srcset=\"https:\/\/complex-systems-ai.com\/wp-content\/uploads\/2020\/03\/nsgaii.png 463w, https:\/\/complex-systems-ai.com\/wp-content\/uploads\/2020\/03\/nsgaii-204x300.png 204w\" sizes=\"(max-width: 463px) 100vw, 463px\" \/><\/figure>\n\n<p class=\"has-text-align-justify\">The SortByRankAndDistance function ranks the population in a hierarchy of non-dominated Pareto fronts. The CrowdingDistance-Assignment calculates the average distance between the members of each front on the front itself. The Crossover-AndMutation function performs the classic genetic crossover and mutation operators of the genetic algorithm. The SelectParentsBy-RankAndDistance and SortByRankAndDistance functions first discriminate the members of the population by their rank (order of priority dominated by the front to which the solution belongs) then by the distance inside the front (calculated by CrowdingDistanceAssignment).<\/p>\n\n<p class=\"has-text-align-justify\">The NSGA non-dominated genetic sorting algorithm was designed and adapted to instances of continuous-function multiple-objective optimization problems. A binary representation can be used in conjunction with classical genetic operators such as point crossing and point mutation. A real-value representation is recommended for continuous function optimization problems, in turn requiring representation-specific genetic operators such as simulated binary crossover (SBX) and polynomial mutation.<\/p>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>","protected":false},"excerpt":{"rendered":"<p>Evolution Algorithms Home Wiki NSGA Non-Dominated Genetic Sort Algorithm The goal of the NSGA Non-Dominated Genetic Sort Algorithm is to improve adaptive fit\u2026 <\/p>","protected":false},"author":1,"featured_media":0,"parent":7110,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-7691","page","type-page","status-publish","hentry"],"amp_enabled":true,"_links":{"self":[{"href":"https:\/\/complex-systems-ai.com\/en\/wp-json\/wp\/v2\/pages\/7691","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/complex-systems-ai.com\/en\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/complex-systems-ai.com\/en\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/complex-systems-ai.com\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/complex-systems-ai.com\/en\/wp-json\/wp\/v2\/comments?post=7691"}],"version-history":[{"count":3,"href":"https:\/\/complex-systems-ai.com\/en\/wp-json\/wp\/v2\/pages\/7691\/revisions"}],"predecessor-version":[{"id":18877,"href":"https:\/\/complex-systems-ai.com\/en\/wp-json\/wp\/v2\/pages\/7691\/revisions\/18877"}],"up":[{"embeddable":true,"href":"https:\/\/complex-systems-ai.com\/en\/wp-json\/wp\/v2\/pages\/7110"}],"wp:attachment":[{"href":"https:\/\/complex-systems-ai.com\/en\/wp-json\/wp\/v2\/media?parent=7691"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}