Beautiful Mind: a meta-heuristic algorithm for generating minimal covering array

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Abstract Today, the application of meta-heuristic algorithms in solving problems is very important. This importance has led to the development of hundreds of types of meta-heuristic algorithms by researchers. The reason for the high number of such algorithms is that an algorithm may be superior to its competitors in a particular problem. Generating a test set in Combinatorial Testing (CT) is one of the thousands of problems that can be solved by meta-heuristic algorithms and hundreds of algorithms have been proposed in this regard. The main challenge in producing a test set in CT is becoming trapped in local optima that several solutions have been offered to overcome this problem. Since the proposed solutions are very slow in terms of time, it is still possible to produce better results by applying other solutions. Continuing our research in the field of CT, we have tried to present a new meta-heuristic solution called Beautiful Mind (BM), which simulates the human way to reach the answer. In fact, the proposed algorithm considers the human intelligence and emotional coefficient to find the answer. The evaluation results show that the proposed approach is much stronger than the existing solutions.
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Beautiful Mind: a meta-heuristic algorithm for generating minimal covering array | 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 Beautiful Mind: a meta-heuristic algorithm for generating minimal covering array Sajad Esfandyari, Vahid Rafe, Einollah Pira, liela Yousofvand This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3195308/v2 This work is licensed under a CC BY 4.0 License Status: Posted Version 2 posted You are reading this latest preprint version Show more versions Abstract Today, the application of meta-heuristic algorithms in solving problems is very important. This importance has led to the development of hundreds of types of meta-heuristic algorithms by researchers. The reason for the high number of such algorithms is that an algorithm may be superior to its competitors in a particular problem. Generating a test set in Combinatorial Testing (CT) is one of the thousands of problems that can be solved by meta-heuristic algorithms and hundreds of algorithms have been proposed in this regard. The main challenge in producing a test set in CT is becoming trapped in local optima that several solutions have been offered to overcome this problem. Since the proposed solutions are very slow in terms of time, it is still possible to produce better results by applying other solutions. Continuing our research in the field of CT, we have tried to present a new meta-heuristic solution called Beautiful Mind (BM), which simulates the human way to reach the answer. In fact, the proposed algorithm considers the human intelligence and emotional coefficient to find the answer. The evaluation results show that the proposed approach is much stronger than the existing solutions. Beautiful Mind (BM) Combinatorial Testing (CT) Coverage Array (CA) Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 2 posted You are reading this latest preprint version Show more versions 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. 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