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approach to evolutionary multiobjective optimization, that avoids most of the time consuming global calculations typical of other multi-objective evolutionary techniques. The new approach uses a simple uniform selection strategy within a steady-state evolutionary algorithm (EA) and employs a straightforward elitist mechanism for replacing population members with their offspring. Global calculations for fitness and Pareto dominance are not needed. Other state-of-the-art Pareto-based EAs depend heavily on various fitness functions and niche evaluations, mostly based on Pareto dominance, and the calculations involved tend to be rather time consuming (at least O(N 2) for a population size, N). The new approach has performed well on some benchmark combinatorial problems and continuous functions, outperforming the latest state-of-the-art EAs in several cases. In this chapter the new approach will be explained in detail.","publication_date":"2005,,","publication_name":"Advanced Information and Knowledge Processing","grobid_abstract_attachment_id":"68068782"},"document_type":"paper","pre_hit_view_count_baseline":null,"quality":"high","language":"en","title":"A Simple Approach to Evolutionary Multiobjective Optimization","broadcastable":true,"draft":null,"has_indexable_attachment":true,"indexable":true}}["work"]; window.loswp.workCoauthors = [93431394]; window.loswp.locale = "en"; window.loswp.countryCode = "SG"; window.loswp.cwvAbTestBucket = ""; window.loswp.designVariant = "ds_vanilla"; window.loswp.fullPageMobileSutdModalVariant = "full_page_mobile_sutd_modal"; 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":68068782,"attachmentType":"pdf"}"><img alt="First page of “A Simple Approach to Evolutionary Multiobjective Optimization”" class="ds-work-cover--cover-thumbnail" src="https://0.academia-photos.com/attachment_thumbnails/68068782/mini_magick20210713-18623-4f4uu1.png?1626204463" /><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">A Simple Approach to Evolutionary Multiobjective Optimization</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="93431394" href="https://cardiff.academia.edu/ChristineMumford"><img alt="Profile image of Christine Mumford" class="ds-work-card--author-avatar" src="//a.academia-assets.com/images/s65_no_pic.png" />Christine Mumford</a></div><div class="ds-work-card--detail"><p class="ds-work-card--detail ds2-5-body-sm">2005, Advanced Information and Knowledge Processing</p></div><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":68068782,"attachmentType":"pdf","workUrl":"https://www.academia.edu/49871799/A_Simple_Approach_to_Evolutionary_Multiobjective_Optimization"}">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":68068782,"attachmentType":"pdf","workUrl":"https://www.academia.edu/49871799/A_Simple_Approach_to_Evolutionary_Multiobjective_Optimization"}"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span>Download PDF</button></div></div></div></div><div data-auto_select="false" data-client_id="331998490334-rsn3chp12mbkiqhl6e7lu2q0mlbu0f1b" data-doc_id="68068782" data-landing_url="https://www.academia.edu/49871799/A_Simple_Approach_to_Evolutionary_Multiobjective_Optimization" data-login_uri="https://www.academia.edu/registrations/google_one_tap" data-moment_callback="onGoogleOneTapEvent" id="g_id_onload"></div><div class="ds-top-related-works--grid-container"><div class="ds-related-content--container ds-top-related-works--container"><h2 class="ds-related-content--heading">Related papers</h2><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="0" data-entity-id="102102677" 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/102102677/Pareto_based_continuous_evolutionary_algorithms_for_multiobjective_optimization">Pareto‐based continuous evolutionary algorithms 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="1013807" href="https://independent.academia.edu/MunboShim">Munbo Shim</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Engineering Computations, 2002</p><p class="ds-related-work--abstract ds2-5-body-sm">In an attempt to solve multiobjective optimization problems, many traditional methods scalarize an objective vector into a single objective by a weight vector. 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href="https://www.academia.edu/2863608/The_pareto_archived_evolution_strategy_A_new_baseline_algorithm_for_pareto_multiobjective_optimisation"><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="52057934" 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/52057934/Multiobjective_Optimization_Using_The_Niche_Pareto_Genetic_Algorithm">Multiobjective Optimization Using The Niche Pareto Genetic 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="306012" href="https://nmu.academia.edu/JeffreyHorn">Jeffrey Horn</a></div><div class="ds-related-work--ctas"><button class="ds2-5-text-link 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