Balancing exploration and exploitation in genetic algorithm with a novel selection operator

Balancing exploration and exploitation in genetic algorithm with a novel selection operator | 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 Research Article Balancing exploration and exploitation in genetic algorithm with a novel selection operator Fahad Rafique, Hengjian Cui This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3739487/v2 This work is licensed under a CC BY 4.0 License Status: Posted Version 2 posted You are reading this latest preprint version Show more versions Abstract The genetic algorithm (GA) is a widely recognized optimization technique for addressing complex and NP-hard problems; however, its effectiveness is often hampered by issues such as premature convergence and population diversity. The selection operator, which determines the probability of individuals being chosen for reproduction, significantly influences its performance. This study presents a novel selection operator, which is specifically designed to strike a balance between selection pressure and the preservation of population diversity. At the first stage, we derived the mathematical properties and assessed the sampling accuracy using Pearson’s chi-square test. For global investigation, we test its performance on benchmark TSPLIB instances and a real-world dataset of Pakistan's city coordinates in the context of the traveling salesman problem (TSP). The results indicate that the newly proposed operator either outperforms or equals the performance of other operators across key metrics, demonstrating superior stability as the dataset size increases. These findings suggest that our operator presents a promising solution for large-scale optimization challenges, such as TSP and other real-world applications. Applied Statistics Genetic algorithm traveling salesman problem selection schemes selection pressure population diversity statistical analysis. Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 2 posted You are reading this latest preprint version Show more versions 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-3739487","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":259499950,"identity":"d0ef44fb-e085-4b70-ac1a-124e7f6b74b2","order_by":0,"name":"Fahad Rafique","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAElEQVRIiWNgGAWjYBAC9gYeBgYgSgCyDRgYKoAUM3MDXi08B1C0nAFpYSRFC2MbSIyQFvazBz+8+WWXxz8jedtn3nm10fztQC0/Krbh1sKTlyw5ty+5WOJGWvFs3m3Hc2ccZmxg7DlzG6cWe4YcA2nengOJDTdyjJl5tx3LbQBqYWZsw62Fh/+N8W+QlvlgLXOO5c4nqEUix0ya58eBxA1gLQ01uRsIa3ljZjm3ITlx45lnxYxzjh3I3QjUchCfX3j4c4xvvPljlzjvePJmhjc1dbnzzh8++OBHBW4tYACJDgYGJh6Gw2DGAfzqQeAPVOsPhjrCikfBKBgFo2DEAQDxMV+jcSjT4wAAAABJRU5ErkJggg==","orcid":"","institution":"Capital Normal University","correspondingAuthor":true,"prefix":"","firstName":"Fahad","middleName":"","lastName":"Rafique","suffix":""},{"id":259499951,"identity":"574c26a5-8f8d-4aa5-a183-39e4325e3c8b","order_by":1,"name":"Hengjian Cui","email":"","orcid":"","institution":"Capital Normal University","correspondingAuthor":false,"prefix":"","firstName":"Hengjian","middleName":"","lastName":"Cui","suffix":""}],"badges":[],"createdAt":"2023-12-11 15:29:26","currentVersionCode":2,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-3739487/v2","doiUrl":"https://doi.org/10.21203/rs.3.rs-3739487/v2","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":75458190,"identity":"ad6fcfd9-4d08-49ac-bcda-25adfcb99dbd","added_by":"auto","created_at":"2025-02-04 21:20:21","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1205955,"visible":true,"origin":"","legend":"","description":"","filename":"paper1revision2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3739487/v2_covered_f9736b77-72a7-4973-97c2-8f2d356f9df9.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eBalancing exploration and exploitation in genetic algorithm with a novel selection operator\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","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":"Genetic algorithm; traveling salesman problem; selection schemes; selection pressure; population diversity; statistical analysis.","lastPublishedDoi":"10.21203/rs.3.rs-3739487/v2","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3739487/v2","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe genetic algorithm (GA) is a widely recognized optimization technique for addressing complex and NP-hard problems; however, its effectiveness is often hampered by issues such as premature convergence and population diversity. 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