CARO: Chaotic Artificial Rabbit Optimization Algorithm for Feature Selection

CARO: Chaotic Artificial Rabbit Optimization Algorithm for 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 Article CARO: Chaotic Artificial Rabbit Optimization Algorithm for Feature Selection Mohammed Abdelrazek, A. H. El-Baz, Mohamed Abd Elaziz This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7302703/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 A modified version of Artificial Rabbit Optimization Algorithm (ARO) for feature selection is proposed in this paper. The ARO method is one of the swarm intelligence algorithms that mimic survival strategies of rabbits in nature. This research introduces an enhanced Artificial Rabbit Optimization (ARO) method, termed CARO, which incorporates ten chaotic maps to improve feature selection performance. Utilizing a wrapper-based approach, CARO aims to identify optimal features that maximize classification accuracy. By integrating chaotic maps to modify key elements of the optimization process, the method seeks to accelerate convergence and enhance algorithmic effectiveness. The proposed method's performance was rigorously evaluated using ten diverse UCI datasets and compared against three prominent meta-heuristic techniques: Ant Colony Optimization (ACO), Whale Optimization Algorithm (WOA), and Particle Swarm Optimization (PSO). Experimental results demonstrate that CARO consistently outperform the comparative meta-heuristic methods in feature selection. Besides, the proposed method is evaluated using 12 functions from CEC’2022 benchmarks where it outperformed the existing methods. Physical sciences/Engineering Physical sciences/Mathematics and computing Feature selection Chaotic maps Artificial Rabbit Optimization Algorithm (ARO) Meta-heuristic Full Text Additional Declarations No competing interests reported. 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-7302703","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":524592385,"identity":"4d0eef69-836a-4e9f-8e01-82ab43e47231","order_by":0,"name":"Mohammed Abdelrazek","email":"","orcid":"","institution":"Damietta University","correspondingAuthor":false,"prefix":"","firstName":"Mohammed","middleName":"","lastName":"Abdelrazek","suffix":""},{"id":524592386,"identity":"62931cec-e76c-42e0-a96d-64b1f9ac9800","order_by":1,"name":"A. H. 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Selection","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":"Feature selection, Chaotic maps, Artificial Rabbit Optimization Algorithm (ARO), 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The ARO method is one of the swarm intelligence algorithms that mimic survival strategies of rabbits in nature. This research introduces an enhanced Artificial Rabbit Optimization (ARO) method, termed CARO, which incorporates ten chaotic maps to improve feature selection performance. Utilizing a wrapper-based approach, CARO aims to identify optimal features that maximize classification accuracy. By integrating chaotic maps to modify key elements of the optimization process, the method seeks to accelerate convergence and enhance algorithmic effectiveness. The proposed method's performance was rigorously evaluated using ten diverse UCI datasets and compared against three prominent meta-heuristic techniques: Ant Colony Optimization (ACO), Whale Optimization Algorithm (WOA), and Particle Swarm Optimization (PSO). Experimental results demonstrate that CARO consistently outperform the comparative meta-heuristic methods in feature selection. 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