{"id":429,"date":"2016-07-14T17:26:07","date_gmt":"2016-07-14T20:26:07","guid":{"rendered":"https:\/\/sbia.org.br\/lnlm\/?page_id=429"},"modified":"2016-07-14T17:26:07","modified_gmt":"2016-07-14T20:26:07","slug":"vol6-no2-art3","status":"publish","type":"page","link":"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol6-no2\/vol6-no2-art3\/","title":{"rendered":"Algoritmo Auxiliar Paralelo para Melhorar a Performance dos Algoritmos Gen\u00e9ticos com Codifica\u00e7\u00e3o Bin\u00e1ria"},"content":{"rendered":"

T\u00edtulo:<\/strong> Algoritmo Auxiliar Paralelo para Melhorar a Performance dos Algoritmos Gen\u00e9ticos com Codifica\u00e7\u00e3o Bin\u00e1ria<\/p>\n

Autores:<\/strong> Camilo J\u00fanior, Celso G.; Yamanaka, Keiji<\/p>\n

Resumo:<\/strong> Some techniques are applied to the optimization problems, however, just a few achieve satisfactory performance when the problem is complex, for example, multimodal or multiobjective. The metaheuristics, although not guaranteeing a global optimum, have good results and, hence, are quite used to these scenarios. Among the metaheuristics, the evolutionary algorithms, especially the Genetic Algorithms (GA), have great results and, hence, one of the most popular. However, the process of improving the solution of an AG may be slow, especially in cases of great complexity. Hence, some papers are developed to improve the performance of the AG. However, when it speeds up the process of evolution in evolutionary algorithms, normally increases the risk of premature convergence, which can negatively influence the population to maximum and minimum locations. Therefore, this work suggests the Assistant Parallel Algorithm (AAP), an algorithm to assist the evolution process of binary encoding GAs. The proposed algorithm is a module attached to the AGs that feeds the population of good individuals. Four operators were created for the AAP: AR, EAR-T, EAR-P and EAR-N, all functionally independent. Experiments were done to measure the efficiency of the AAP and its operators. The results show that the AAP reach the objective of assist the good evolution without using specifics knowledges about the problem.<\/p>\n

Palavras-chave:<\/strong> Algoritmos Gen\u00e9ticos; Converg\u00eancia Prematura; Velocidade de Converg\u00eancia; Computa\u00e7\u00e3o Evolucion\u00e1ria; Metaheur\u00edstica<\/p>\n

P\u00e1ginas:<\/strong> 21<\/p>\n

C\u00f3digo DOI:<\/strong> 10.21528\/lmln-vol6-no2-art3<\/a><\/p>\n

Artigo em PDF:<\/strong> vol6-no2-art3.pdf<\/a><\/p>\n

Arquivo BibTex:<\/strong> vol6-no2-art3.bib<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

T\u00edtulo: Algoritmo Auxiliar Paralelo para Melhorar a Performance dos Algoritmos Gen\u00e9ticos com Codifica\u00e7\u00e3o Bin\u00e1ria Autores: Camilo J\u00fanior, Celso G.; Yamanaka, Keiji Resumo: Some techniques are applied to the optimization problems, however, just a few achieve satisfactory performance when the problem Read More …<\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"parent":423,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-429","page","type-page","status-publish","hentry"],"yoast_head":"\nAlgoritmo Auxiliar Paralelo para Melhorar a Performance dos Algoritmos Gen\u00e9ticos com Codifica\u00e7\u00e3o Bin\u00e1ria - 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\/vol6-no2\/vol6-no2-art3\/\" \/>\n<meta property=\"og:locale\" content=\"pt_BR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Algoritmo Auxiliar Paralelo para Melhorar a Performance dos Algoritmos Gen\u00e9ticos com Codifica\u00e7\u00e3o Bin\u00e1ria - Learning and NonLinear Models\" \/>\n<meta property=\"og:description\" content=\"T\u00edtulo: Algoritmo Auxiliar Paralelo para Melhorar a Performance dos Algoritmos Gen\u00e9ticos com Codifica\u00e7\u00e3o Bin\u00e1ria Autores: Camilo J\u00fanior, Celso G.; 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