An Approach Using BRKGA for Optimizing Graph Neural Network Architectures

An Approach Using BRKGA for Optimizing Graph Neural Network Architectures | 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 An Approach Using BRKGA for Optimizing Graph Neural Network Architectures Andersson Alves da Silva, Ricardo Martins de Abreu Silva, Paulo Oliva This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7467591/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Graph Neural Networks (GNNs) have emerged as powerful tools for modeling graph-structured data, demonstrating success in node classification, link prediction, and graph classification. However, designing optimal GNN architectures remains a challenging and resource-intensive task due to the complexity of hyperparameter choices and the high-dimensional nature of graph data. Neural Architecture Search (NAS) has been proposed as a solution to automate this process, with evolutionary algorithms showing particular promise in exploring large architectural spaces. This paper introduces the Biased Random-Key Genetic Algorithm for Graph Neural Networks (BRKGA-GNN), a novel framework that applies BRKGA to optimize GNN architectures. BRKGA offers a robust and efficient search mechanism for navigating the architecture space by leveraging biased random keys and evolutionary strategies. To validate the effectiveness of BRKGA-GNN, we conducted experiments on three widely used benchmark datasets, comparing their performance against state-of-the-art NAS-based methods. The proposed methodology provides an automated and scalable solution for designing effective GNN architectures while minimizing reliance on manual tuning. This study highlights the potential of BRKGA-GNN to advance research in both NAS and GNN optimization, inviting further exploration of its applications and benefits. Graph Neural Networks Neural Architecture Search Biased Random-Key Genetic Algorithm Hyperparameter Optimization Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 21 Nov, 2025 Reviews received at journal 16 Nov, 2025 Reviewers agreed at journal 28 Oct, 2025 Reviewers agreed at journal 24 Oct, 2025 Reviews received at journal 24 Sep, 2025 Reviewers agreed at journal 24 Sep, 2025 Reviewers invited by journal 21 Sep, 2025 Editor assigned by journal 27 Aug, 2025 Submission checks completed at journal 27 Aug, 2025 First submitted to journal 26 Aug, 2025 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-7467591","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":520286678,"identity":"918f850d-30eb-4761-bdc2-00cdc65ec022","order_by":0,"name":"Andersson Alves da Silva","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAu0lEQVRIiWNgGAWjYHACxgcfGCTANJA4QJQWZsMZYC3MBkRrYZPmgWglUgt/+/Fn0jY1FnkMEslsQBfeySeoReJMjrF1zjGJYqAWdqALn1k2ENRzIIfxdm6DRGKDRP4xoAsPGxDUIX/++QNpS7CWZDbpP8RoMbiRYCTNCNPCQIwWwxtvjA17jkkktvE8ZjfsMXhGWIvc+fSHD37U1CX2swND7EfFHcJa4IANjEjQANM1CkbBKBgFowALAADxZzgHJXPyZwAAAABJRU5ErkJggg==","orcid":"","institution":"Federal University of Pernambuco","correspondingAuthor":true,"prefix":"","firstName":"Andersson","middleName":"Alves da","lastName":"Silva","suffix":""},{"id":520286680,"identity":"3425c93e-bd4c-4479-be21-c3e881ec6403","order_by":1,"name":"Ricardo Martins de Abreu Silva","email":"","orcid":"","institution":"Federal University of Pernambuco","correspondingAuthor":false,"prefix":"","firstName":"Ricardo","middleName":"Martins de Abreu","lastName":"Silva","suffix":""},{"id":520286682,"identity":"8220a845-ae5b-4bf6-bbac-a9b251fb4d97","order_by":2,"name":"Paulo Oliva","email":"","orcid":"","institution":"Queen Mary University of London","correspondingAuthor":false,"prefix":"","firstName":"Paulo","middleName":"","lastName":"Oliva","suffix":""}],"badges":[],"createdAt":"2025-08-27 04:08:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7467591/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7467591/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":92558988,"identity":"21116c9b-fa54-4154-844d-9b3a5cce3b94","added_by":"auto","created_at":"2025-10-01 04:01:13","extension":"json","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":5743,"visible":true,"origin":"","legend":"","description":"","filename":"c3910b1015504dc082d59d95b72aa2a8.json","url":"https://assets-eu.researchsquare.com/files/rs-7467591/v1/b665d77bb6b8a41bf438f62a.json"},{"id":92560160,"identity":"b2ed92f3-a29d-4885-b90c-8e2b1b4c9a06","added_by":"auto","created_at":"2025-10-01 04:09:23","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3549796,"visible":true,"origin":"","legend":"","description":"","filename":"BRKGAGNNEvolutionaryIntelligencesubmission.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7467591/v1_covered_b65a92a8-f715-4da2-934f-f3b6a8716761.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"An Approach Using BRKGA for Optimizing Graph Neural Network Architectures","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":"evolutionary-intelligence","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"evin","sideBox":"Learn more about [Evolutionary Intelligence](http://link.springer.com/journal/12065)","snPcode":"12065","submissionUrl":"https://submission.nature.com/new-submission/12065/3","title":"Evolutionary Intelligence","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Graph Neural Networks, Neural Architecture Search, Biased Random-Key Genetic Algorithm, Hyperparameter Optimization","lastPublishedDoi":"10.21203/rs.3.rs-7467591/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7467591/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Graph Neural Networks (GNNs) have emerged as powerful tools for modeling graph-structured data, demonstrating success in node classification, link prediction, and graph classification. 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