Self-Adaptive Learning Using Test Case Selection in Grammatical Evolution for Automatic Design of Digital Circuit

Self-Adaptive Learning Using Test Case Selection in Grammatical Evolution for Automatic Design of Digital Circuit | 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 Self-Adaptive Learning Using Test Case Selection in Grammatical Evolution for Automatic Design of Digital Circuit Krishn Kumar Gupt, Meghana Kshirsagar, Rajkumar Sarma, Joe Sullivan, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4701667/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 In today’s digital age, the demand for increasingly complex digital circuits spans applications ranging from watches to spacecraft. To meet this demand, circuit design automation using Artificial Intelligence (AI) has emerged as a key solution due to manual processes’ time-consuming and error-prone nature. Among AI techniques, Evolutionary Algorithm (EA), notably Grammatical Evolution(GE), have shown promise. However, the computational cost of these algorithms remains a significant challenge, particularly concerning scalability and the high volume of test cases required for circuit design and synthesis. Balancing the trade-off between the necessity for extensive training data and the computational overhead of GE is crucial. The exponential increase in data volume with circuit complexity presents a challenging obstacle. While utilizing complete training data may ensure accurate circuit design, practical constraints often limit its feasibility. Furthermore, reducing data through selection risks compromising crucial information, while using complete data incurs prohibitively high training costs. This study proposes a novel self-adaptive learning approach within the framework of EAs to utilize training data adaptively and effectively. Our objective is to achieve comparable circuit solutions within a reduced runtime. We apply this technique to GE, naming it Self-adaptive Learning in GE (SLIG). SLIG divides the training data into subsets based on decreasing diversity, ensuring that the model is exposed to highly diverse scenarios early in the evolutionary process and gradually incorporating less diverse data. This method is rigorously evaluated across four digital circuits. The circuits are designed using Hardware Description Language (HDL), facilitating precise and efficient design, simulation, and synthesis. Experimental and statistical results indicate its effectiveness in producing comparable or superior results compared to standard GE, as measured in terms of circuits’ test scores, solution size, runtime efficiency, and performance parameters evaluated by the Cadence Genus Synthesis solution tool. Adaptive Learning Grammatical Evolution Circuit Design Logic Synthesis Cadence Genus 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. 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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-4701667","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":327108591,"identity":"3e5d0063-5165-4802-a9c8-486c4309409a","order_by":0,"name":"Krishn Kumar Gupt","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA10lEQVRIiWNgGAWjYBACCQbGBmYom/EBAwMzAxsJWpiZDYjUAlIG0cImAWfjA5KzDzd/LtzBYLfhdv+xap4aawY+9gb8WqT5EtukZ55hSN5w5zDbbZ5j6QxsPAfwa5HjYWxj5m1jSDa4kcx2m7fhMAObRAJBLc2fYVqKwVrkHxBwGA9jgzRQix1ICzPEFvw6GCR7GNuAWiQSJO8cNpaccyydh42HgMMkzrA/BjrMxp7vduPDD29qrOXk2w8QsAaqM7EB6h4eotSDgD0DIS+MglEwCkbByAUA2cI3Ii1f6+YAAAAASUVORK5CYII=","orcid":"","institution":"University College Dublin","correspondingAuthor":true,"prefix":"","firstName":"Krishn","middleName":"Kumar","lastName":"Gupt","suffix":""},{"id":327108592,"identity":"49c5e456-3a86-4699-b663-d37d243e2241","order_by":1,"name":"Meghana Kshirsagar","email":"","orcid":"","institution":"University of Limerick","correspondingAuthor":false,"prefix":"","firstName":"Meghana","middleName":"","lastName":"Kshirsagar","suffix":""},{"id":327108593,"identity":"ea7159d6-3fd2-4982-88a0-a29cb41ffa26","order_by":2,"name":"Rajkumar Sarma","email":"","orcid":"","institution":"University of Limerick","correspondingAuthor":false,"prefix":"","firstName":"Rajkumar","middleName":"","lastName":"Sarma","suffix":""},{"id":327108596,"identity":"4404067c-0e8d-4afb-93e5-86f55f9846be","order_by":3,"name":"Joe Sullivan","email":"","orcid":"","institution":"Technological University of the Shannon: Midlands Midwest","correspondingAuthor":false,"prefix":"","firstName":"Joe","middleName":"","lastName":"Sullivan","suffix":""},{"id":327108597,"identity":"100ab89d-67b1-419f-8fc2-9b63a6ec4d36","order_by":4,"name":"Conor Ryan","email":"","orcid":"","institution":"University of Limerick","correspondingAuthor":false,"prefix":"","firstName":"Conor","middleName":"","lastName":"Ryan","suffix":""}],"badges":[],"createdAt":"2024-07-07 21:38:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4701667/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4701667/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":65202272,"identity":"0cd2fd6c-8b3d-4f7b-a2fe-5b34e345317d","added_by":"auto","created_at":"2024-09-24 17:01:51","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1598028,"visible":true,"origin":"","legend":"","description":"","filename":"SLIGKrishn.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4701667/v1_covered_9f529286-7226-448d-9d9e-836b7efa6314.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Self-Adaptive Learning Using Test Case Selection in Grammatical Evolution for Automatic Design of Digital Circuit","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":"Adaptive Learning, Grammatical Evolution, Circuit Design, Logic Synthesis, Cadence Genus","lastPublishedDoi":"10.21203/rs.3.rs-4701667/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4701667/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"In today’s digital age, the demand for increasingly complex digital circuits spans applications ranging from watches to spacecraft. To meet this demand, circuit design automation using Artificial Intelligence (AI) has emerged as a key solution due to manual processes’ time-consuming and error-prone nature. Among AI techniques, Evolutionary Algorithm (EA), notably Grammatical Evolution(GE), have shown promise. However, the computational cost of these algorithms remains a significant challenge, particularly concerning scalability and the high volume of test cases required for circuit design and synthesis. Balancing the trade-off between the necessity for extensive training data and the computational overhead of GE is crucial. The exponential increase in data volume with circuit complexity presents a challenging obstacle. While utilizing complete training data may ensure accurate circuit design, practical constraints often limit its feasibility. Furthermore, reducing data through selection risks compromising crucial information, while using complete data incurs prohibitively high training costs. This study proposes a novel self-adaptive learning approach within the framework of EAs to utilize training data adaptively and effectively. Our objective is to achieve comparable circuit solutions within a reduced runtime. We apply this technique to GE, naming it Self-adaptive Learning in GE (SLIG). SLIG divides the training data into subsets based on decreasing diversity, ensuring that the model is exposed to highly diverse scenarios early in the evolutionary process and gradually incorporating less diverse data. This method is rigorously evaluated across four digital circuits. The circuits are designed using Hardware Description Language (HDL), facilitating precise and efficient design, simulation, and synthesis. 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