Metaheuristics Meets Statistics: Cuckoo Search for Designs, Inference, and Data Geometry

Metaheuristics Meets Statistics: Cuckoo Search for Designs, Inference, and Data Geometry | 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 Metaheuristics Meets Statistics: Cuckoo Search for Designs, Inference, and Data Geometry Elvis Han Cui, Heather Xihe Yu, Guanghao Qi, Weng Kee Wong This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7438415/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 Nature-inspired metaheuristic algorithms have grown by leaps and bounds in the last three decades, and there are now many new and powerful algorithms with increasing ability to tackle all kinds of optimization problems across disciplines. This paper shows the diverse and new applications of a typical nature-inspired metaheuristic algorithm called Cuckoo or Cuckoo Search (CS). As examples, we demonstrate applications of the CS algorithm to find optimal exact designs for estimating model parameters in a nonlinear regression model, tackle complicated estimation problems in bioinformatics, and how CS efficiently constructs principal curves to visualize and analyze a large cloud of data. Optimal design Emax model Michaelis-Menten model Principal curves Pseudotime 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-7438415","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":505026791,"identity":"e004369b-7bde-4660-bb29-652442f95418","order_by":0,"name":"Elvis Han Cui","email":"","orcid":"","institution":"University of California, Los Angeles","correspondingAuthor":false,"prefix":"","firstName":"Elvis","middleName":"Han","lastName":"Cui","suffix":""},{"id":505026792,"identity":"b15a6267-5d50-4bc0-a9ed-f2bcb4db821a","order_by":1,"name":"Heather Xihe Yu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA40lEQVRIie3NIQvCQBTA8SfCLNOrb4hTv8HJihb9KhsD08LiQNEDQcvAavQjDATzyYGWgfWiyWSzmEROMLpbFLx/uHvh/XgAJtOPJgDQJe8xVo9fivQ9h6mRliRVgCTIeFlCZdQTcYLB7ry18EGHQGoR1ZCxLzY5ent5sZyUhuCkNx0JuagvsbWX3GoCraq7OhIwUX9iZbd5kzmM9ERdYdjNCFNEAEUNcfKrL+wjeigri0FKTzbm17iQNE6Rd7enM5esxUE+kolLVmFWSDr8M2HA1GcXrqva7DMR/n3LZDKZ/rsXUZVHKu2nHlIAAAAASUVORK5CYII=","orcid":"","institution":"University of California, Los Angeles","correspondingAuthor":true,"prefix":"","firstName":"Heather","middleName":"Xihe","lastName":"Yu","suffix":""},{"id":505026793,"identity":"25021171-a9dd-4b93-a9d7-c57b4a8161a1","order_by":2,"name":"Guanghao Qi","email":"","orcid":"","institution":"University of Washington","correspondingAuthor":false,"prefix":"","firstName":"Guanghao","middleName":"","lastName":"Qi","suffix":""},{"id":505026794,"identity":"8667b4d4-3799-49c1-9439-bde861825f82","order_by":3,"name":"Weng Kee Wong","email":"","orcid":"","institution":"University of California, Los Angeles","correspondingAuthor":false,"prefix":"","firstName":"Weng","middleName":"Kee","lastName":"Wong","suffix":""}],"badges":[],"createdAt":"2025-08-23 04:08:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7438415/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7438415/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":100721182,"identity":"4654c4cb-3cf0-4ac6-862a-9c57ce11d266","added_by":"auto","created_at":"2026-01-20 19:44:48","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3256405,"visible":true,"origin":"","legend":"","description":"","filename":"MetaheuristicsMeetsStatisticsStatsComputing.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7438415/v1_covered_c59f7367-cb94-4c6f-a068-ffd3b62c1060.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Metaheuristics Meets Statistics: Cuckoo Search for Designs, Inference, and Data Geometry","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":"Optimal design, Emax model, Michaelis-Menten model, Principal curves, Pseudotime","lastPublishedDoi":"10.21203/rs.3.rs-7438415/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7438415/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Nature-inspired metaheuristic algorithms have grown by leaps and bounds in the last three decades, and there are now many new and powerful algorithms with increasing ability to tackle all kinds of optimization problems across disciplines. This paper shows the diverse and new applications of a typical nature-inspired metaheuristic algorithm called Cuckoo or Cuckoo Search (CS). As examples, we demonstrate applications of the CS algorithm to find optimal exact designs for estimating model parameters in a nonlinear regression model, tackle complicated estimation problems in bioinformatics, and how CS efficiently constructs principal curves to visualize and analyze a large cloud of data.","manuscriptTitle":"Metaheuristics Meets Statistics: Cuckoo Search for Designs, Inference, and Data Geometry","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-02 04:08:24","doi":"10.21203/rs.3.rs-7438415/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"29c67edb-576f-4d48-a440-f123f131620a","owner":[],"postedDate":"September 2nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-20T16:38:38+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-02 04:08:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7438415","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7438415","identity":"rs-7438415","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}