Stagnation in Evolutionary Algorithms: Convergence ≠ Optimality

Stagnation in Evolutionary Algorithms: Convergence ≠ Optimality | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Short Report Stagnation in Evolutionary Algorithms: Convergence ≠ Optimality Xiaojun Zhou This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6574782/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract In the evolutionary computation community, it is widely believed that stagnation impedes convergence in evolutionary algorithms, and that convergence inherently indicates optimality. However, this perspective is misleading. In this study, it is the first to highlight that the stagnation of an individual can actually facilitate the convergence of the entire population, and convergence does not necessarily imply optimality, not even local optimality. Convergence alone is insufficient to ensure the effectiveness of evolutionary algorithms. Several counterexamples are provided to illustrate this argument. Artificial Intelligence and Machine Learning Evolutionary Algorithm Convergence Stagnation Counterexample Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6574782","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Short Report","associatedPublications":[],"authors":[{"id":450830150,"identity":"91eb8b08-c74f-4143-bf3b-9e756ff30335","order_by":0,"name":"Xiaojun Zhou","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/UlEQVRIiWNgGAWjYDACZgY2IGkBYoIYNhBRHsJaJGBa0ojQwoCq5TBhLbrtzM8e/KiQYDA4wP7swce28/LyMxIYH7xtY5A3x6HF7DCbuWHPGQkGyQaGdMOZbbcNN9xIYDac28ZguLMBlxYeNgneNgkGfgaGY9K8bbcTDCQS2IAMhgSDA7i1SP79JwH0CGMbUOW5BKDD2H8T0iLN2wCyhRlk+IEEhhsJbMz4tbCZScsck+CRbGZjk5xxLtlww5mHzZJzzkkYbsCl5fzhZ5JvamzkDI63P5P4UGYnL9+efPDDmzIbeVy2wAAPME5hgLGBARpTo2AUjIJRMArIBAB5kkuN60O/RgAAAABJRU5ErkJggg==","orcid":"","institution":"Central South University","correspondingAuthor":true,"prefix":"","firstName":"Xiaojun","middleName":"","lastName":"Zhou","suffix":""}],"badges":[],"createdAt":"2025-05-02 01:37:34","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-6574782/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6574782/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":81926492,"identity":"a12aaa1b-adfd-4033-81c9-597f3f64fcaf","added_by":"auto","created_at":"2025-05-05 03:43:28","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":450959,"visible":true,"origin":"","legend":"","description":"","filename":"Stagnationissues.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6574782/v1_covered_b534424d-1b3a-4ae8-a9ad-016759e3d05d.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eStagnation in Evolutionary Algorithms: Convergence ≠ Optimality\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[{"identity":"095690e0-3828-40b0-8e31-191fdcc20703","identifier":"10.13039/501100001809","name":"National Natural Science Foundation of China","awardNumber":"62273357","order_by":0}],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Central South University","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Evolutionary Algorithm, Convergence, Stagnation, Counterexample","lastPublishedDoi":"10.21203/rs.3.rs-6574782/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6574782/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn the evolutionary computation community, it is widely believed that stagnation impedes convergence in evolutionary algorithms, and that convergence inherently indicates optimality. 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