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This results in a more knowledgeable decision. However, multimodal solutions and nearly optimal solutions are ignored, although their consideration may be useful for the decision\u2010maker. In particular, there are some of these solutions which we consider specially interesting, namely, the ones that have distinct characteristics from those which dominate them (i.e., the solutions that are not dominated in their neighborhood). We call these solutions <jats:italic>potentially useful<\/jats:italic> solutions. In this work, a new genetic algorithm called nevMOGA is presented, which provides not only the optimal solutions but also the multimodal and nearly optimal solutions nondominated in their neighborhood. This means that nevMOGA is able to supply additional and potentially useful solutions for the decision\u2010making stage. This is its main advantage. In order to assess its performance, nevMOGA is tested on two benchmarks and compared with two other optimization algorithms (random and exhaustive searches). Finally, as an example of application, nevMOGA is used in an engineering problem to optimally adjust the parameters of two PI controllers that operate a plant.<\/jats:p>","DOI":"10.1155\/2018\/1792420","type":"journal-article","created":{"date-parts":[[2018,10,2]],"date-time":"2018-10-02T23:30:34Z","timestamp":1538523034000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["A Multiobjective Genetic Algorithm for the Localization of Optimal and Nearly Optimal Solutions Which Are Potentially Useful: nevMOGA"],"prefix":"10.1155","volume":"2018","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2036-2709","authenticated-orcid":false,"given":"Alberto","family":"Pajares","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9737-2833","authenticated-orcid":false,"given":"Xavier","family":"Blasco","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1914-7494","authenticated-orcid":false,"given":"Juan M.","family":"Herrero","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8392-6225","authenticated-orcid":false,"given":"Gilberto","family":"Reynoso-Meza","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2018,10,2]]},"reference":[{"key":"e_1_2_9_1_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4615-5563-6"},{"volume-title":"Multi-Objective Optimization Using Evolutionary Algorithms","year":"2001","author":"Deb K.","key":"e_1_2_9_2_2"},{"key":"e_1_2_9_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.riai.2013.04.001"},{"key":"e_1_2_9_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2014.07.009"},{"key":"e_1_2_9_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2007.09.018"},{"key":"e_1_2_9_6_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-88908-3"},{"key":"e_1_2_9_7_2","doi-asserted-by":"crossref","unstructured":"DebK.andSahaA. 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