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This task normally demands knowledge from previous experience, as the higher amount of required computational resources makes trial and error approaches prohibitive. Evolutionary computation algorithms have shown success in many domains, by guiding the exploration of complex solution spaces in the direction of the best solutions, with minimal human intervention. In this sense, this work presents the use of genetic algorithms in deep neural networks topology selection. The evaluated algorithms were able to find competitive topologies while spending less computational resources when compared to state-of-the-art methods.<\/jats:p>","DOI":"10.1155\/2019\/3217542","type":"journal-article","created":{"date-parts":[[2019,1,15]],"date-time":"2019-01-15T18:33:11Z","timestamp":1547577191000},"page":"1-12","source":"Crossref","is-referenced-by-count":10,"title":["An Experiment on the Use of Genetic Algorithms for Topology Selection in Deep Learning"],"prefix":"10.1155","volume":"2019","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2601-5449","authenticated-orcid":true,"given":"Fernando","family":"Mattioli","sequence":"first","affiliation":[{"name":"Faculty of Electrical Engineering, Federal University of Uberl\u00e2ndia, Uberl\u00e2ndia, MG, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Daniel","family":"Caetano","sequence":"additional","affiliation":[{"name":"Faculty of Electrical Engineering, Federal University of Uberl\u00e2ndia, Uberl\u00e2ndia, MG, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alexandre","family":"Cardoso","sequence":"additional","affiliation":[{"name":"Faculty of Electrical Engineering, Federal University of Uberl\u00e2ndia, Uberl\u00e2ndia, MG, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Eduardo","family":"Naves","sequence":"additional","affiliation":[{"name":"Faculty of Electrical Engineering, Federal University of Uberl\u00e2ndia, Uberl\u00e2ndia, MG, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Edgard","family":"Lamounier","sequence":"additional","affiliation":[{"name":"Faculty of Electrical Engineering, Federal University of Uberl\u00e2ndia, Uberl\u00e2ndia, MG, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","reference":[{"doi-asserted-by":"publisher","key":"1","DOI":"10.1038\/nature14539"},{"volume":"1","year":"2017","key":"2"},{"year":"2016","key":"3"},{"key":"6","first-page":"281","volume":"13","year":"2012","journal-title":"Journal of Machine Learning Research"},{"doi-asserted-by":"publisher","key":"7","DOI":"10.1109\/tgrs.2017.2698503"},{"doi-asserted-by":"publisher","key":"9","DOI":"10.1016\/j.asoc.2017.12.036"},{"year":"1992","key":"10"},{"doi-asserted-by":"publisher","key":"11","DOI":"10.1109\/tnn.2002.804317"},{"doi-asserted-by":"publisher","key":"12","DOI":"10.1162\/106365602320169811"},{"year":"2009","key":"16"},{"year":"1998","key":"17"},{"doi-asserted-by":"publisher","key":"20","DOI":"10.1109\/5.726791"},{"volume-title":"Learning multiple layers of features from tiny images","year":"2009","key":"21"}],"container-title":["Journal of Electrical and Computer Engineering"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/jece\/2019\/3217542.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/jece\/2019\/3217542.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/jece\/2019\/3217542.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,2,24]],"date-time":"2019-02-24T18:30:58Z","timestamp":1551033058000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.hindawi.com\/journals\/jece\/2019\/3217542\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,1,15]]},"references-count":13,"alternative-id":["3217542","3217542"],"URL":"https:\/\/doi.org\/10.1155\/2019\/3217542","relation":{},"ISSN":["2090-0147","2090-0155"],"issn-type":[{"type":"print","value":"2090-0147"},{"type":"electronic","value":"2090-0155"}],"subject":[],"published":{"date-parts":[[2019,1,15]]}}}