{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,6]],"date-time":"2026-04-06T10:24:38Z","timestamp":1775471078146,"version":"3.50.1"},"reference-count":96,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2023,3,20]],"date-time":"2023-03-20T00:00:00Z","timestamp":1679270400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>The efficiency and the effectiveness of a machine learning (ML) model are greatly influenced by feature selection (FS), a crucial preprocessing step in machine learning that seeks out the ideal set of characteristics with the maximum accuracy possible. Due to their dominance over traditional optimization techniques, researchers are concentrating on a variety of metaheuristic (or evolutionary) algorithms and trying to suggest cutting-edge hybrid techniques to handle FS issues. The use of hybrid metaheuristic approaches for FS has thus been the subject of numerous research works. The purpose of this paper is to critically assess the existing hybrid FS approaches and to give a thorough literature review on the hybridization of different metaheuristic\/evolutionary strategies that have been employed for supporting FS. This article reviews pertinent documents on hybrid frameworks that were published in the period from 2009 to 2022 and offers a thorough analysis of the used techniques, classifiers, datasets, applications, assessment metrics, and schemes of hybridization. Additionally, new open research issues and challenges are identified to pinpoint the areas that have to be further explored for additional study.<\/jats:p>","DOI":"10.3390\/a16030167","type":"journal-article","created":{"date-parts":[[2023,3,20]],"date-time":"2023-03-20T04:33:31Z","timestamp":1679286811000},"page":"167","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Literature Review on Hybrid Evolutionary Approaches for Feature Selection"],"prefix":"10.3390","volume":"16","author":[{"given":"Jayashree","family":"Piri","sequence":"first","affiliation":[{"name":"Department of CSE, GITAM Institute of Technology (Deemed to be University), Visakhapatnam 530045, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1718-1640","authenticated-orcid":false,"given":"Puspanjali","family":"Mohapatra","sequence":"additional","affiliation":[{"name":"International Institute of Information Technology, Bhubaneswar 751003, India"}]},{"given":"Raghunath","family":"Dey","sequence":"additional","affiliation":[{"name":"School of Computer Engineering, KIIT (Deemed to be University), Bhubaneswar 751024, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6506-9207","authenticated-orcid":false,"given":"Biswaranjan","family":"Acharya","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering-AI, Marwadi University, Rajkot 360003, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9895-7606","authenticated-orcid":false,"given":"Vassilis C.","family":"Gerogiannis","sequence":"additional","affiliation":[{"name":"Department of Digital Systems, University of Thessaly, 382 21 Larissa, Greece"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9964-4134","authenticated-orcid":false,"given":"Andreas","family":"Kanavos","sequence":"additional","affiliation":[{"name":"Department of Informatics, Ionian University, 491 00 Corfu, Greece"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Piri, J., Mohapatra, P., and Dey, R. 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