ANÁLISE DO DESEMPENHO DE MÉTODOS DE INTELIGÊNCIA ARTIFICIAL BASEADOS NO COMPORTAMENTO DAS PLANTAS

• Ano de defesa: 2017
• Autor(a) principal: AZEVEDO, Marília Marta Gomes Orquiza de
• Orientador(a): PAUCAR, Vicente Leonardo
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal do Maranhão
• Programa de Pós-Graduação: PROGRAMA DE PÓS-GRADUAÇÃO EM ENGENHARIA DE ELETRICIDADE/CCET
• Departamento: DEPARTAMENTO DE ENGENHARIA DA ELETRICIDADE/CCET
• País: Brasil
• Palavras-chave em Português: Inteligência artificial; inteligência das plantas; algoritmo de polinização das flores; algoritmo de propagação do morango; algoritmo da colonização das ervas daninhas
• Palavras-chave em Inglês: Artificial intelligence;intelligence of plants; flower pollination algorithm; strawberry propagation algorithm; weed colonization algorithm
• Área do conhecimento CNPq: Computabilidade e Modelos de Computação
• Link de acesso: http://tedebc.ufma.br:8080/jspui/handle/tede/1252

Artificial intelligence (AI) is a branch of computer science that studies the intelligent behavior of living beings, and mimics this intelligence by deploying it in computer programs, machines and systems in order to solve problems related to searching, optimization, planning, control, automation, etc. One of the areas of artificial intelligence is evolutionary computation, which is inspired by the principle of natural evolution of species. Within the evolutionary computation several methods based on the intelligence of plants have been recently proposed. How the plants survive and adapt in harsh environments has aroused great interest of researchers in AI. It is remarkable that the life cycle of a plant is extremely intriguing. The way the plants reproduce, propagate, disperse their seeds and select the most resistant is undoubtedly an evidence of intelligence of plants when optimize their existence. In this sense, several computer algorithms based on the intelligent lifecycle of plants have been proposed recently, these algorithms are in many cases, simple to implement, and very efficient in solving complex problems. In this work, the performance of some algorithms, the flower pollination algorithm, strawberry plant algorithm, invasive weed optimization and plant life cycle algorithm, all of them based on the intelligent behavior of plants, are analyzed when applied to optimization of test functions, and they are also compared with classical genetic algorithms.