Archive-based multiple feature construction method using adaptive Genetic Programming

Archive-based multiple feature construction method using adaptive Genetic Programming | 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 Archive-based multiple feature construction method using adaptive Genetic Programming Kaixuan Jia, Fan Zhang, Xiaoying Gao, Jianbin Ma This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4966994/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 12 May, 2025 Read the published version in Memetic Computing → Version 1 posted 9 You are reading this latest preprint version Abstract The quality of features is an important factor that affects the classification performance of machine learning algorithms. Feature construction based on Genetic Programming (GP) can automatically create more discriminative features, sometimes greatly improving classification performance. However, constructing a single feature or a small number of features may make the linkage information between labels and features insufficient, resulting in poor classification performance, so we introduce a multi-feature construction method. In addition, premature convergence of the GP may also affect classification performance. This paper proposes an archive-based multiple feature construction method which uses elite archive strategy to preserve and select effective constructed features, and employs an adaptive strategy for GP to adjust the crossover and mutation probabilities based on fitness values. Experiments on ten datasets show that our proposed archive-based multiple feature construction method without using adaptive GP can significantly improve the classification performance compared with traditional single feature construction method, and the classification performance can be maintained or further improved by adding the adaptive strategy. The comparisons with three state-of-the-art techniques show that our proposed method can significantly achieve better classification performance. Genetic Programming Feature construction Adaptive Multiple features Classification Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 12 May, 2025 Read the published version in Memetic Computing → Version 1 posted Editorial decision: Revision requested 24 Nov, 2024 Reviews received at journal 11 Nov, 2024 Reviews received at journal 06 Nov, 2024 Reviewers agreed at journal 30 Oct, 2024 Reviewers agreed at journal 29 Oct, 2024 Reviewers invited by journal 02 Oct, 2024 Editor assigned by journal 02 Oct, 2024 Submission checks completed at journal 28 Aug, 2024 First submitted to journal 23 Aug, 2024 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. 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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-4966994","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":346127342,"identity":"08bd5d52-79fc-46a2-888e-94ac2cb08211","order_by":0,"name":"Kaixuan Jia","email":"","orcid":"","institution":"College of Information Science and Technology,Hebei Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Kaixuan","middleName":"","lastName":"Jia","suffix":""},{"id":346127343,"identity":"beb943be-5da3-4da7-b314-ec0dae5ab352","order_by":1,"name":"Fan Zhang","email":"","orcid":"","institution":"College of Information Science and Technology,Hebei Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Fan","middleName":"","lastName":"Zhang","suffix":""},{"id":346127344,"identity":"c112ad03-d0d0-4d1a-a386-0cf7134f5d8e","order_by":2,"name":"Xiaoying Gao","email":"","orcid":"","institution":"Victoria University of Wellington","correspondingAuthor":false,"prefix":"","firstName":"Xiaoying","middleName":"","lastName":"Gao","suffix":""},{"id":346127345,"identity":"7f109a79-1ea9-4827-af18-054f9d680407","order_by":3,"name":"Jianbin Ma","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA70lEQVRIiWNgGAWjYHCChAMMDAd4GNh7GJihAsRq4TlDvBYQAOqSyCFSi277gYcHfu64I2Mu+fbg44Kawwz87DkGDD934NZidiYh4WDvmWc8lrPzko1nHDvMINnzxoCx9wweLQcSEg7wth3mMbidYybNw3aYweBGjgEzYxseLecfJBz8C9Jy84z5b55/hxnsCWq5kZBwGGzLDR4zZiCDwUCCoJYHCYdl257xGJzJMZae2ZfOI3HmWcHBXrwOy0n++Lbtjr3B8TOGnwu+WcvxtydvfPATjxYGBp4EVC6IOIBPAwMDOwH5UTAKRsEoGAUA25pZWVp1cMYAAAAASUVORK5CYII=","orcid":"","institution":"College of Information Science and Technology,Hebei Agricultural University","correspondingAuthor":true,"prefix":"","firstName":"Jianbin","middleName":"","lastName":"Ma","suffix":""}],"badges":[],"createdAt":"2024-08-24 03:33:59","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4966994/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4966994/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s12293-025-00453-7","type":"published","date":"2025-05-12T15:57:27+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":83067785,"identity":"40d20168-dce9-4c0e-86ef-371ce943bbf0","added_by":"auto","created_at":"2025-05-19 16:06:05","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1003958,"visible":true,"origin":"","legend":"","description":"","filename":"memeticcomputing.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4966994/v1_covered_61f1156e-d983-4870-b940-8f346e7522ba.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Archive-based multiple feature construction method using adaptive Genetic Programming","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"memetic-computing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"meme","sideBox":"Learn more about [Memetic Computing](http://link.springer.com/journal/12289)","snPcode":"12293","submissionUrl":"https://submission.nature.com/new-submission/12293/3","title":"Memetic Computing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Genetic Programming,Feature construction,Adaptive,Multiple features, Classification","lastPublishedDoi":"10.21203/rs.3.rs-4966994/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4966994/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The quality of features is an important factor that affects the classification performance of machine learning algorithms. Feature construction based on Genetic Programming (GP) can automatically create more discriminative features, sometimes greatly improving classification performance. However, constructing a single feature or a small number of features may make the linkage information between labels and features insufficient, resulting in poor classification performance, so we introduce a multi-feature construction method. In addition, premature convergence of the GP may also affect classification performance. This paper proposes an archive-based multiple feature construction method which uses elite archive strategy to preserve and select effective constructed features, and employs an adaptive strategy for GP to adjust the crossover and mutation probabilities based on fitness values. 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