{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,8]],"date-time":"2026-01-08T17:30:41Z","timestamp":1767893441926,"version":"3.49.0"},"reference-count":66,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2017,11,2]],"date-time":"2017-11-02T00:00:00Z","timestamp":1509580800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Fonds de Recherche du Qu\u00e9bec - Nature et Technologies","award":["189555"],"award-info":[{"award-number":["189555"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>This article presents a newly proposed selection process for genetic algorithms on a class of unconstrained optimization problems. The k-means genetic algorithm selection process (KGA) is composed of four essential stages: clustering, membership phase, fitness scaling and selection. Inspired from the hypothesis that clustering the population helps to preserve a selection pressure throughout the evolution of the population, a membership probability index is assigned to each individual following the clustering phase. Fitness scaling converts the membership scores in a range suitable for the selection function which selects the parents of the next generation. Two versions of the KGA process are presented: using a fixed number of clusters K (KGAf) and via an optimal partitioning Kopt (KGAo) determined by two different internal validity indices. The performance of each method is tested on seven benchmark problems.<\/jats:p>","DOI":"10.3390\/a10040123","type":"journal-article","created":{"date-parts":[[2017,11,3]],"date-time":"2017-11-03T04:43:13Z","timestamp":1509684193000},"page":"123","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":33,"title":["A Selection Process for Genetic Algorithm Using Clustering Analysis"],"prefix":"10.3390","volume":"10","author":[{"given":"Adam","family":"Chehouri","sequence":"first","affiliation":[{"name":"Universit\u00e9 du Qu\u00e9bec \u00e0 Chicoutimi, 555 boulevard de l\u2019Universit\u00e9, Chicoutimi, QC G7H 2B1, Canada"},{"name":"Faculty of Engineering, Third Branch, Lebanese University, Rafic Harriri Campus, Hadath, Beirut, Lebanon"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7957-2057","authenticated-orcid":false,"given":"Rafic","family":"Younes","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, Third Branch, Lebanese University, Rafic Harriri Campus, Hadath, Beirut, Lebanon"}]},{"given":"Jihan","family":"Khoder","sequence":"additional","affiliation":[{"name":"LISV Laboratory, University of Versailles Saint-Quentin en-Yvelines, 10-12 Avenue de l\u2019Europe, 78140 V\u00e9lizy, France"}]},{"given":"Jean","family":"Perron","sequence":"additional","affiliation":[{"name":"Universit\u00e9 du Qu\u00e9bec \u00e0 Chicoutimi, 555 boulevard de l\u2019Universit\u00e9, Chicoutimi, QC G7H 2B1, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8236-2317","authenticated-orcid":false,"given":"Adrian","family":"Ilinca","sequence":"additional","affiliation":[{"name":"Wind Energy Research Laboratory (WERL), Universit\u00e9 du Qu\u00e9bec \u00e0 Rimouski, 300 all\u00e9e des Ursulines, Rimouski, QC G5L 3A1, Canada"}]}],"member":"1968","published-online":{"date-parts":[[2017,11,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"15","DOI":"10.3390\/a7010015","article-title":"Bio-Inspired Meta-Heuristics for Emergency Transportation Problems","volume":"7","author":"Zhang","year":"2014","journal-title":"Algorithms"},{"key":"ref_2","unstructured":"Fister, I., Yang, X.-S., Fister, I., Brest, J., and Fister, D. 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