{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T05:53:22Z","timestamp":1769925202220,"version":"3.49.0"},"reference-count":29,"publisher":"MIT Press - Journals","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Evolutionary Computation"],"published-print":{"date-parts":[[2015,3]]},"abstract":"<jats:p> Structured evolutionary algorithms have been investigated for some time. However, they have been under explored especially in the field of multi-objective optimization. Despite good results, the use of complex dynamics and structures keep the understanding and adoption rate of structured evolutionary algorithms low. Here, we propose a general subpopulation framework that has the capability of integrating optimization algorithms without restrictions as well as aiding the design of structured algorithms. The proposed framework is capable of generalizing most of the structured evolutionary algorithms, such as cellular algorithms, island models, spatial predator-prey, and restricted mating based algorithms. Moreover, we propose two algorithms based on the general subpopulation framework, demonstrating that with the simple addition of a number of single-objective differential evolution algorithms for each objective, the results improve greatly, even when the combined algorithms behave poorly when evaluated alone at the tests. Most importantly, the comparison between the subpopulation algorithms and their related panmictic algorithms suggests that the competition between different strategies inside one population can have deleterious consequences for an algorithm and reveals a strong benefit of using the subpopulation framework. <\/jats:p>","DOI":"10.1162\/evco_a_00118","type":"journal-article","created":{"date-parts":[[2014,1,17]],"date-time":"2014-01-17T18:53:45Z","timestamp":1389984825000},"page":"1-36","source":"Crossref","is-referenced-by-count":15,"title":["General Subpopulation Framework and Taming the Conflict Inside Populations"],"prefix":"10.1162","volume":"23","author":[{"given":"Danilo Vasconcellos","family":"Vargas","sequence":"first","affiliation":[{"name":"Graduate School of Information Science and Electrical Engineering, Kyushu University, Fukuoka, 819-0395, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junichi","family":"Murata","sequence":"additional","affiliation":[{"name":"Faculty of Information Science and Electrical Engineering, Kyushu University, Fukuoka, 819-0395, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hirotaka","family":"Takano","sequence":"additional","affiliation":[{"name":"Faculty of Information Science and Electrical Engineering, Kyushu University, Fukuoka, 819-0395, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alexandre Cl\u00e1udio Botazzo","family":"Delbem","sequence":"additional","affiliation":[{"name":"Institute of Mathematics and Computer Science, University of S\u00e3o Paulo, S\u00e3o Carlos, 13566-590, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"281","reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.1109\/TEVC.2002.800880"},{"key":"B2","doi-asserted-by":"publisher","DOI":"10.1109\/TEVC.2009.2015575"},{"key":"B3","doi-asserted-by":"publisher","DOI":"10.1109\/TEVC.2006.872133"},{"key":"B4","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-68830-3"},{"key":"B5","doi-asserted-by":"publisher","DOI":"10.1109\/MCI.2006.1597059"},{"key":"B7","doi-asserted-by":"publisher","DOI":"10.1109\/4235.996017"},{"key":"B8","doi-asserted-by":"publisher","DOI":"10.1109\/TPWRS.2004.840442"},{"key":"B9","doi-asserted-by":"publisher","DOI":"10.1109\/CEC.2010.5586100"},{"key":"B10","first-page":"5","volume":"2009","author":"Doncieux S.","year":"2009","journal-title":"Workshop on Exploring New Horizons in Evolutionary Design of Robots at IROS"},{"key":"B11","doi-asserted-by":"publisher","DOI":"10.1109\/TEVC.2009.2034647"},{"key":"B12","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-61723-X_1022"},{"key":"B14","doi-asserted-by":"publisher","DOI":"10.1007\/s10732-008-9080-4"},{"key":"B15","volume-title":"Genetic algorithms in search, optimization, and machine learning","author":"Goldberg D. 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