{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:36:27Z","timestamp":1760146587311,"version":"build-2065373602"},"reference-count":49,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2024,11,21]],"date-time":"2024-11-21T00:00:00Z","timestamp":1732147200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Israeli Science Foundation","award":["2714\/19"],"award-info":[{"award-number":["2714\/19"]}]},{"name":"Israeli Smart Transportation Research Center (ISTRC)","award":["2714\/19"],"award-info":[{"award-number":["2714\/19"]}]},{"name":"Israeli Council for Higher Education (CHE) via the Data Science Research Center, Ben-Gurion University of the Negev, Israel","award":["2714\/19"],"award-info":[{"award-number":["2714\/19"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>We present a novel approach to performing fitness approximation in genetic algorithms (GAs) using machine learning (ML) models, focusing on dynamic adaptation to the evolutionary state. We compare different methods for (1) switching between actual and approximate fitness, (2) sampling the population, and (3) weighting the samples. Experimental findings demonstrate significant improvement in evolutionary runtimes, with fitness scores that are either identical or slightly lower than those of the fully run GA\u2014depending on the ratio of approximate-to-actual-fitness computation. Although we focus on evolutionary agents in Gymnasium (game) simulators\u2014where fitness computation is costly\u2014our approach is generic and can be easily applied to many different domains.<\/jats:p>","DOI":"10.3390\/info15120744","type":"journal-article","created":{"date-parts":[[2024,11,21]],"date-time":"2024-11-21T12:24:51Z","timestamp":1732191891000},"page":"744","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Fitness Approximation Through Machine Learning with Dynamic Adaptation to the Evolutionary State"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-5019-017X","authenticated-orcid":false,"given":"Itai","family":"Tzruia","sequence":"first","affiliation":[{"name":"Department of Computer Science, Ben-Gurion University, Beer-Sheva 8410501, Israel"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-9050-8118","authenticated-orcid":false,"given":"Tomer","family":"Halperin","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Ben-Gurion University, Beer-Sheva 8410501, Israel"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1811-472X","authenticated-orcid":false,"given":"Moshe","family":"Sipper","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Ben-Gurion University, Beer-Sheva 8410501, Israel"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4009-5353","authenticated-orcid":false,"given":"Achiya","family":"Elyasaf","sequence":"additional","affiliation":[{"name":"Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva 8410501, Israel"}]}],"member":"1968","published-online":{"date-parts":[[2024,11,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1038\/scientificamerican0792-66","article-title":"Genetic Algorithms","volume":"267","author":"Holland","year":"1992","journal-title":"Sci. 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