{"id":282,"date":"2016-07-13T16:57:19","date_gmt":"2016-07-13T19:57:19","guid":{"rendered":"https:\/\/sbia.org.br\/lnlm\/?page_id=282"},"modified":"2016-07-13T16:57:19","modified_gmt":"2016-07-13T19:57:19","slug":"vol2-no2-art2","status":"publish","type":"page","link":"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol2-no2\/vol2-no2-art2\/","title":{"rendered":"Parallel genetic algorithm with different evolution behavior for multilayer perceptrons design and learning"},"content":{"rendered":"<p><strong>T\u00edtulo:<\/strong> Parallel genetic algorithm with different evolution behavior for multilayer perceptrons design and learning<\/p>\n<p><strong>Autores:<\/strong> Albuquerque, Ana Claudia M. L.; Melo, Jorge D.; D\u00f3ria Neto, Adri\u00e3o D.<\/p>\n<p align=\"justify\"><strong>Resumo:<\/strong> The combination of genetic algorithms and parallel processing can be very powerful when applied to the design and learning of Multilayer Perceptron neural networks. It is well known that the definition of Multilayer Perceptron neural networks is a very difficult task that could become, to a large extent, a process of trial and error. Also, the learning process of Multilayer Perceptron neural networks can be very slow, putting in danger the performance of countless applications. Therefore techniques for automating the design of neural networks are clearly of interest and the use of parallel processing is essential in minimizing the time required on the learning process. Furthermore, the use of cooperation in the genetic algorithm allows the interaction of different populations, avoiding local minima and helping in the search of the ideal solution. Finally, the use of exclusive evolution behavior on each copy of the genetic algorithm running in each task helped in enhancing the diversity of populations and the search for the fit solution.<\/p>\n<p><strong>Palavras-chave:<\/strong> Neural networks; parallel processing; evolutionary computation; genetic algorithms<\/p>\n<p><strong>P\u00e1ginas:<\/strong> 11<\/p>\n<p><strong>C\u00f3digo DOI:<\/strong> <a href=\"http:\/\/dx.doi.org\/10.21528\/lnlm-vol2-no2-art2\">10.21528\/lmln-vol2-no2-art2<\/a><\/p>\n<p><strong>Artigo em PDF:<\/strong> <a href=\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/sites\/4\/2016\/07\/vol2-no2-art2.pdf\" rel=\"\">vol2-no2-art2.pdf<\/a><\/p>\n<p><strong>Arquivo BibTex:<\/strong> <a href=\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/sites\/4\/2016\/07\/vol2-no2-art2.bib\" rel=\"\">vol2-no2-art2.bib<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>T\u00edtulo: Parallel genetic algorithm with different evolution behavior for multilayer perceptrons design and learning Autores: Albuquerque, Ana Claudia M. L.; Melo, Jorge D.; D\u00f3ria Neto, Adri\u00e3o D. Resumo: The combination of genetic algorithms and parallel processing can be very powerful <a href=\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol2-no2\/vol2-no2-art2\/\" class=\"read-more\">Read More &#8230;<\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"parent":278,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-282","page","type-page","status-publish","hentry"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.9 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Parallel genetic algorithm with different evolution behavior for multilayer perceptrons design and learning - Learning and NonLinear Models<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol2-no2\/vol2-no2-art2\/\" \/>\n<meta property=\"og:locale\" content=\"pt_BR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Parallel genetic algorithm with different evolution behavior for multilayer perceptrons design and learning - Learning and NonLinear Models\" \/>\n<meta property=\"og:description\" content=\"T\u00edtulo: Parallel genetic algorithm with different evolution behavior for multilayer perceptrons design and learning Autores: Albuquerque, Ana Claudia M. L.; Melo, Jorge D.; D\u00f3ria Neto, Adri\u00e3o D. 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L.; Melo, Jorge D.; D\u00f3ria Neto, Adri\u00e3o D. 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