Aprendizagem estrutural de redes bayesianas utilizando algoritmo genético multi-agente

• Ano de defesa: 2021
• Autor(a) principal: Itallo Guilherme Machado
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Minas Gerais; Brasil; ENG - DEPARTAMENTO DE ENGENHARIA ELÉTRICA; Programa de Pós-Graduação em Engenharia Elétrica; UFMG
• Palavras-chave em Português: Redes bayesiana; Aprendizado estrutural; Algoritmo genético; Algoritmo genético multiagente; Engenharia elétrica; Algoritmos genéticos
• Link de acesso: http://hdl.handle.net/1843/38359

This work approaches the structural learning of Bayesian networks. The task of learning a Bayesian Network from data is an Np-hard problem. We present the Multi-Agent Genetic Algorithm (MAGA) to learn Bayesian network structures. The MAGA algorithm uses the elements of a multi-agent system, such as communication between agents, their interaction with the environment, and the mechanisms of genetic algorithms that subject agents to genetic operators. In this study, two configurations of MAGA were implemented, which were compared to other algorithms in the literature. The purpose of this research is to evaluate the performance of the MAGA, mainly in instances with many variables and large volumes of data. The experiment results show that the two configurations of MAGA are effective in learning the Bayesian Network structures, chiefly in instances with many variables and large volumes of data.