AMPA-FS: An Adaptive Multi-strategy Marine Predators Algorithm for Wrapper-based Feature Selection

AMPA-FS: An Adaptive Multi-strategy Marine Predators Algorithm for Wrapper-based Feature Selection | 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 AMPA-FS: An Adaptive Multi-strategy Marine Predators Algorithm for Wrapper-based Feature Selection Bingchi Sun This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9458849/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 14 You are reading this latest preprint version Abstract Feature selection (FS) is essential for high-dimensional classification. The Marine Predators Algorithm (MPA) couples L\'evy and Brownian random walks in a three-phase foraging model, yet its binary variant (BMPA) suffers from (i) over-aggressive step-size decay during exploration, (ii) uneven search-space coverage from uniform initialization, and (iii) no explicit mechanism for escaping local optima. This paper proposes AMPA-FS, integrating three synergistic modules: (1) Tent chaotic map initialization with elite opposition-based learning; (2) a phase-aligned adaptive step-size control factor matching MPA's three phases; and (3) a stagnation-triggered elite-guided differential mutation operator. Experiments on ten UCI datasets against nine binary metaheuristics show that AMPA-FS achieves 5 wins, 5 ties, and 0 losses against BMPA in classification accuracy with no regression on any dataset, ranks 3rd in wrapper-fitness Friedman rank among all ten algorithms, and selects 17--28% fewer features than swarm-based competitors on datasets with D ≥30. Vargha--Delaney effect-size analysis confirms that AMPA-FS provides small-to-medium practical improvements over the majority of competitors on the wrapper-fitness objective. A complete 2 3 −1 ablation study quantifies each module's individual and synergistic contribution, showing that the full model achieves the best average fitness rank across all variants. Feature selection Marine Predators Algorithm Metaheuristic optimization Multi-strategy enhancement Wrapper-based classification Full Text Additional Declarations No competing interests reported. Supplementary Files supplementary.zip Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 18 May, 2026 Reviews received at journal 11 May, 2026 Reviews received at journal 29 Apr, 2026 Reviewers agreed at journal 28 Apr, 2026 Reviews received at journal 26 Apr, 2026 Reviewers agreed at journal 24 Apr, 2026 Reviews received at journal 24 Apr, 2026 Reviewers agreed at journal 23 Apr, 2026 Reviewers agreed at journal 23 Apr, 2026 Reviewers agreed at journal 23 Apr, 2026 Reviewers invited by journal 23 Apr, 2026 Editor assigned by journal 20 Apr, 2026 Submission checks completed at journal 19 Apr, 2026 First submitted to journal 18 Apr, 2026 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. 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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-9458849","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":629761482,"identity":"52c57ab0-9022-4e91-a930-56b62c900279","order_by":0,"name":"Bingchi Sun","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7klEQVRIiWNgGAWjYNACg388/MzMBw58qJCQkyesnBmIKw7ISba3JT6cccbC2LCBKC1nDhgbnDljbMzZVpHIcICABnP2/oOPK9vuJG64kZYmzThPIoGxgfnhoxt4tFj2HGY2PNv2LHHmjeRj0oXbJPLYGdiMjXPwaDG4kcwm2djGnNgHsmXmNolixgYeNmm8Wu4/Zv8J0tJwI8dMmneORGLDAUJabjCzMTacOWwsAPI+bwMRWix7ko0lGyrSoIF8TMLYsJmAX8zZDz782GBgA43Kmjo5efbmh4/xOgxTiBmPchxaRsEoGAWjYBSgAQABX1LiZpDnrAAAAABJRU5ErkJggg==","orcid":"","institution":"Zhejiang University of Technology","correspondingAuthor":true,"prefix":"","firstName":"Bingchi","middleName":"","lastName":"Sun","suffix":""}],"badges":[],"createdAt":"2026-04-19 02:55:00","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9458849/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9458849/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108007759,"identity":"7d6f3c3d-6bb7-4477-96bb-930eba3a07b7","added_by":"auto","created_at":"2026-04-28 13:01:47","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":448172,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9458849/v1_covered_3948a7b6-a669-4d96-9329-9b6b9821c618.pdf"},{"id":108003841,"identity":"a7a3e3de-7620-493d-a5e3-2224d3f7e4cd","added_by":"auto","created_at":"2026-04-28 12:26:05","extension":"zip","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":48325,"visible":true,"origin":"","legend":"","description":"","filename":"supplementary.zip","url":"https://assets-eu.researchsquare.com/files/rs-9458849/v1/c7e71730988bafcff010aadd.zip"}],"financialInterests":"No competing interests reported.","formattedTitle":"AMPA-FS: An Adaptive Multi-strategy Marine Predators Algorithm for Wrapper-based Feature Selection","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"international-journal-of-machine-learning-and-cybernetics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jmlc","sideBox":"Learn more about [International Journal of Machine Learning and Cybernetics](http://actavetscand.biomedcentral.com/)","snPcode":"13042","submissionUrl":"https://submission.nature.com/new-submission/13042/3","title":"International Journal of Machine Learning and Cybernetics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Feature selection, Marine Predators Algorithm, Metaheuristic optimization, Multi-strategy enhancement, Wrapper-based classification","lastPublishedDoi":"10.21203/rs.3.rs-9458849/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9458849/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eFeature selection (FS) is essential for high-dimensional classification. The Marine Predators Algorithm (MPA) couples L\\'evy and Brownian random walks in a three-phase foraging model, yet its binary variant (BMPA) suffers from (i) over-aggressive step-size decay during exploration, (ii) uneven search-space coverage from uniform initialization, and (iii) no explicit mechanism for escaping local optima. This paper proposes AMPA-FS, integrating three synergistic modules: (1) Tent chaotic map initialization with elite opposition-based learning; (2) a phase-aligned adaptive step-size control factor matching MPA's three phases; and (3) a stagnation-triggered elite-guided differential mutation operator. Experiments on ten UCI datasets against nine binary metaheuristics show that AMPA-FS achieves 5 wins, 5 ties, and 0 losses against BMPA in classification accuracy with no regression on any dataset, ranks 3rd in wrapper-fitness Friedman rank among all ten algorithms, and selects 17--28% fewer features than swarm-based competitors on datasets with D ≥30. 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