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To this purpose, in" /> <title>(PDF) GeDEA-II : A Novel Evolutionary Algorithm for Multi-Objective Optimization Problems Based on the Simplex Crossover and The Shrink Mutation | Ernesto Benini - Academia.edu</title> <link rel="canonical" href="https://www.academia.edu/65703391/GeDEA_II_A_Novel_Evolutionary_Algorithm_for_Multi_Objective_Optimization_Problems_Based_on_the_Simplex_Crossover_and_The_Shrink_Mutation" /> <script async src="https://www.googletagmanager.com/gtag/js?id=G-5VKX33P2DS"></script> <script> window.dataLayer = window.dataLayer || []; function gtag(){dataLayer.push(arguments);} gtag('js', new Date()); gtag('config', 'G-5VKX33P2DS', { cookie_domain: 'academia.edu', send_page_view: false, }); gtag('event', 'page_view', { 'controller': "single_work", 'action': "show", 'controller_action': 'single_work#show', 'logged_in': 'false', 'edge': 'unknown', // Send nil if there is no A/B test bucket, in case some records get logged // with missing data - that way we can distinguish between the two cases. // ab_test_bucket should be of the form <ab_test_name>:<bucket> 'ab_test_bucket': null, }) </script> <script> var $controller_name = 'single_work'; var $action_name = "show"; var $rails_env = 'production'; var $app_rev = '90022c3fe27cb89122c9303fed524c4bc69eaedb'; var $domain = 'academia.edu'; var $app_host = "academia.edu"; var $asset_host = "academia-assets.com"; var $start_time = new Date().getTime(); var $recaptcha_key = "6LdxlRMTAAAAADnu_zyLhLg0YF9uACwz78shpjJB"; var $recaptcha_invisible_key = "6Lf3KHUUAAAAACggoMpmGJdQDtiyrjVlvGJ6BbAj"; var $disableClientRecordHit = false; </script> <script> window.require = { config: function() { return function() {} } } </script> <script> window.Aedu = window.Aedu || {}; window.Aedu.hit_data = null; window.Aedu.serverRenderTime = new Date(1732722175000); window.Aedu.timeDifference = new Date().getTime() - 1732722175000; </script> <script type="application/ld+json">{"@context":"https://schema.org","@type":"ScholarlyArticle","abstract":"The key issue for an efficient and reliable multiobjective evolutionary algorithm is the ability to converge to the True Pareto Front with the least number of objective function evaluations, while covering it as much as possible. To this purpose, in a previous paper performance comparisons showed that the Genetic Diversity Evolutionary Algorithm (GeDEA) was at the same level of the best state-of-the-art MOEAs due to it intrinsic ability to properly conjugate exploitation of current non-dominated solutions and the exploration of the search space. In this paper, an improved version, namely the GeDEAII, is proposed which features a novel crossover operator, the Simplex-Crossover, and a novel mutation operator, the ShrinkMutation. GeDEM operator was left unchanged and completed using the non-dominated-sorting based on crowding distance. The comparison among GeDEA-II and GeDEA, as well as with three other modern elitist methods, on different extremely multidimensional test problems, clea...","author":[{"@context":"https://schema.org","@type":"Person","name":"Ernesto Benini"}],"contributor":[],"dateCreated":"2021-12-23","dateModified":"2021-12-23","datePublished":"2012-01-01","headline":"GeDEA-II : A Novel Evolutionary Algorithm for Multi-Objective Optimization Problems Based on the Simplex Crossover and The Shrink Mutation","inLanguage":"en","keywords":[],"locationCreated":null,"publication":null,"publisher":{"@context":"https://schema.org","@type":"Organization","name":null},"image":null,"thumbnailUrl":null,"url":"https://www.academia.edu/65703391/GeDEA_II_A_Novel_Evolutionary_Algorithm_for_Multi_Objective_Optimization_Problems_Based_on_the_Simplex_Crossover_and_The_Shrink_Mutation","sourceOrganization":[{"@context":"https://schema.org","@type":"EducationalOrganization","name":"unipd"}]}</script><link rel="stylesheet" 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{"work":{"id":65703391,"created_at":"2021-12-23T07:44:22.464-08:00","from_world_paper_id":188788448,"updated_at":"2022-08-01T06:29:56.877-07:00","_data":{"abstract":"The key issue for an efficient and reliable multiobjective evolutionary algorithm is the ability to converge to the True Pareto Front with the least number of objective function evaluations, while covering it as much as possible. To this purpose, in a previous paper performance comparisons showed that the Genetic Diversity Evolutionary Algorithm (GeDEA) was at the same level of the best state-of-the-art MOEAs due to it intrinsic ability to properly conjugate exploitation of current non-dominated solutions and the exploration of the search space. In this paper, an improved version, namely the GeDEAII, is proposed which features a novel crossover operator, the Simplex-Crossover, and a novel mutation operator, the ShrinkMutation. GeDEM operator was left unchanged and completed using the non-dominated-sorting based on crowding distance. The comparison among GeDEA-II and GeDEA, as well as with three other modern elitist methods, on different extremely multidimensional test problems, clea...","publication_date":"2012,,"},"document_type":"paper","pre_hit_view_count_baseline":null,"quality":"high","language":"en","title":"GeDEA-II : A Novel Evolutionary Algorithm for Multi-Objective Optimization Problems Based on the Simplex Crossover and The Shrink Mutation","broadcastable":true,"draft":null,"has_indexable_attachment":true,"indexable":true}}["work"]; window.loswp.workCoauthors = [202736334]; window.loswp.locale = "en"; window.loswp.countryCode = "SG"; window.loswp.cwvAbTestBucket = ""; window.loswp.designVariant = "ds_vanilla"; window.loswp.fullPageMobileSutdModalVariant = "control"; window.loswp.useOptimizedScribd4genScript = false; window.loswp.appleClientId = 'edu.academia.applesignon';</script><script defer="" src="https://accounts.google.com/gsi/client"></script><div class="ds-loswp-container"><div class="ds-work-card--grid-container"><div class="ds-work-card--container js-loswp-work-card"><div class="ds-work-card--cover"><div class="ds-work-cover--wrapper"><div class="ds-work-cover--container"><button class="ds-work-cover--clickable js-swp-download-button" data-signup-modal="{"location":"swp-splash-paper-cover","attachmentId":77184754,"attachmentType":"pdf"}"><img alt="First page of “GeDEA-II : A Novel Evolutionary Algorithm for Multi-Objective Optimization Problems Based on the Simplex Crossover and The Shrink Mutation”" class="ds-work-cover--cover-thumbnail" src="https://0.academia-photos.com/attachment_thumbnails/77184754/mini_magick20211223-14382-j79724.png?1640274534" /><img alt="PDF Icon" class="ds-work-cover--file-icon" src="//a.academia-assets.com/assets/single_work_splash/adobe.icon-574afd46eb6b03a77a153a647fb47e30546f9215c0ee6a25df597a779717f9ef.svg" /><div class="ds-work-cover--hover-container"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span><p>Download Free PDF</p></div><div class="ds-work-cover--ribbon-container">Download Free PDF</div><div class="ds-work-cover--ribbon-triangle"></div></button></div></div></div><div class="ds-work-card--work-information"><h1 class="ds-work-card--work-title">GeDEA-II : A Novel Evolutionary Algorithm for Multi-Objective Optimization Problems Based on the Simplex Crossover and The Shrink Mutation</h1><div class="ds-work-card--work-authors ds-work-card--detail"><a class="ds-work-card--author js-wsj-grid-card-author ds2-5-body-md ds2-5-body-link" data-author-id="202736334" href="https://unipd.academia.edu/ErnestoBenini"><img alt="Profile image of Ernesto Benini" class="ds-work-card--author-avatar" src="https://0.academia-photos.com/202736334/63817845/52129798/s65_ernesto.benini.png" />Ernesto Benini</a></div><div class="ds-work-card--detail"><p class="ds-work-card--detail ds2-5-body-sm">2012</p></div><p class="ds-work-card--work-abstract ds-work-card--detail ds2-5-body-md">The key issue for an efficient and reliable multiobjective evolutionary algorithm is the ability to converge to the True Pareto Front with the least number of objective function evaluations, while covering it as much as possible. To this purpose, in a previous paper performance comparisons showed that the Genetic Diversity Evolutionary Algorithm (GeDEA) was at the same level of the best state-of-the-art MOEAs due to it intrinsic ability to properly conjugate exploitation of current non-dominated solutions and the exploration of the search space. In this paper, an improved version, namely the GeDEAII, is proposed which features a novel crossover operator, the Simplex-Crossover, and a novel mutation operator, the ShrinkMutation. GeDEM operator was left unchanged and completed using the non-dominated-sorting based on crowding distance. The comparison among GeDEA-II and GeDEA, as well as with three other modern elitist methods, on different extremely multidimensional test problems, clea...</p><div class="ds-work-card--button-container"><button class="ds2-5-button js-swp-download-button" data-signup-modal="{"location":"continue-reading-button--work-card","attachmentId":77184754,"attachmentType":"pdf","workUrl":"https://www.academia.edu/65703391/GeDEA_II_A_Novel_Evolutionary_Algorithm_for_Multi_Objective_Optimization_Problems_Based_on_the_Simplex_Crossover_and_The_Shrink_Mutation"}">See full PDF</button><button class="ds2-5-button ds2-5-button--secondary js-swp-download-button" data-signup-modal="{"location":"download-pdf-button--work-card","attachmentId":77184754,"attachmentType":"pdf","workUrl":"https://www.academia.edu/65703391/GeDEA_II_A_Novel_Evolutionary_Algorithm_for_Multi_Objective_Optimization_Problems_Based_on_the_Simplex_Crossover_and_The_Shrink_Mutation"}"><span class="material-symbols-outlined" 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ds2-5-body-link" href="https://www.academia.edu/98963654/A_New_Multiobjective_Evolutionary_Algorithm_Based_on_Decomposition_of_the_Objective_Space_for_Multiobjective_Optimization">A New Multiobjective Evolutionary Algorithm Based on Decomposition of the Objective Space for Multiobjective Optimization</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="227275069" href="https://independent.academia.edu/CynthiaCortesVillalobos">Cynthia Cortes Villalobos</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Journal of Applied Mathematics, 2014</p><p class="ds-related-work--abstract ds2-5-body-sm">In order to well maintain the diversity of obtained solutions, a new multiobjective evolutionary algorithm based on decomposition of the objective space for multiobjective optimization problems (MOPs) is designed. In order to achieve the goal, the objective space of a MOP is decomposed into a set of subobjective spaces by a set of direction vectors. In the evolutionary process, each subobjective space has a solution, even if it is not a Pareto optimal solution. In such a way, the diversity of obtained solutions can be maintained, which is critical for solving some MOPs. In addition, if a solution is dominated by other solutions, the solution can generate more new solutions than those solutions, which makes the solution of each subobjective space converge to the optimal solutions as far as possible. Experimental studies have been conducted to compare this proposed algorithm with classic MOEA/D and NSGAII. Simulation results on six multiobjective benchmark functions show that the prop...</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"A New Multiobjective Evolutionary Algorithm Based on Decomposition of the Objective Space for Multiobjective Optimization","attachmentId":100174819,"attachmentType":"pdf","work_url":"https://www.academia.edu/98963654/A_New_Multiobjective_Evolutionary_Algorithm_Based_on_Decomposition_of_the_Objective_Space_for_Multiobjective_Optimization","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/98963654/A_New_Multiobjective_Evolutionary_Algorithm_Based_on_Decomposition_of_the_Objective_Space_for_Multiobjective_Optimization"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="6" data-entity-id="83757882" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/83757882/Hybrid_Selection_Based_Multi_Many_Objective_Evolutionary_Algorithm">Hybrid Selection Based Multi/Many-Objective Evolutionary Algorithm</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="56044722" href="https://independent.academia.edu/KedarNathDas">Kedar Nath Das</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2021</p><p class="ds-related-work--abstract ds2-5-body-sm">In the last decade, numerous Multi/Many-Objective Evolutionary Algorithms (MOEAs) have been proposed to handle Multi/Many-Objective Problems (MOPs) with challenges such as discontinuous Pareto Front (PF), degenerate PF, etc. MOEAs in the literature can be broadly divided into three categories based on the selection strategy employed such as dominance, decomposition, and indicator-based MOEAs. Each category of MOEAs have their advantages and disadvantages when solving MOPs with diverse characteristics. In this work, we propose a Hybrid Selection based MOEA, referred to as HS-MOEA, which is a simple yet effective hybridization of dominance, decomposition and indicator-based concepts. In other words, we propose a new environmental selection strategy where the Pareto-dominance, reference vectors and an indicator are combined to effectively balance the diversity and convergence properties of MOEA during the evolution. The superior performance of HS-MOEA compared to the state-of-the-art M...</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Hybrid Selection Based Multi/Many-Objective Evolutionary Algorithm","attachmentId":88999414,"attachmentType":"pdf","work_url":"https://www.academia.edu/83757882/Hybrid_Selection_Based_Multi_Many_Objective_Evolutionary_Algorithm","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/83757882/Hybrid_Selection_Based_Multi_Many_Objective_Evolutionary_Algorithm"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="7" data-entity-id="99879763" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/99879763/Decomposition_Based_Sorting_and_Angle_Based_Selection_for_Evolutionary_Multiobjective_and_Many_Objective_Optimization">Decomposition-Based-Sorting and Angle-Based-Selection for Evolutionary Multiobjective and Many-Objective Optimization</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="15879489" href="https://independent.academia.edu/ZhunFan">Zhun Fan</a></div><p class="ds-related-work--metadata ds2-5-body-xs">IEEE Transactions on Cybernetics, 2017</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" 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