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Tipo de Documento:	Dissertação
Título:	A framework for inverse modeling applied to multi-objective evolutionary algorithms
Autor(es):	Oliveira, Artur Leandro da Costa
Data do documento:	8-Jun-2022
Orientador:	Carvalho, André Britto de
Coorientador:	Gusmão, Renê Pereira de
Resumo:	Many-Objective Optimization Problems (MaOPs) are a class of complex optimization problems deined by having more than three objective functions. Traditional Multi-Objective Evolutionary Algorithms (MOEAs) have shown poor scalability in solving this kind of problem. The use of machine learning techniques to enhance optimization algorithms applied to MaOPs has been drawing attention due to their capacity to add domain knowledge during the search process. One method of this kind is inverse modeling, which uses machine learning models to enhance MOEAs diferently, mapping the objective function values to the decision variables. This method has shown a good performance in diverse optimization problems due to the ability to directly predict solutions closed to the Pareto-optimal front, among these methods, we can highlight the Decision Variable Learning (DVL). The strategies involving inverse models found, including the DVL, have some limitations such as the exploration of the performance of diferent machine learning models and the strategies in using the generated knowledge during the search. The main goal of this work is to create a framework that uses an inverse modeling approach coupled to any MOEA found in the literature. More precisely, three main steps were taken to achieve the goals. First, we perform a systematic review of the literature to identify the main uses of machine learning techniques enhancing optimization algorithms. Secondly, we analyze the performance of diferent machine learning methods in the DVL, seeking to understand the main characteristics of inverse modeling through the DVL algorithm. In the last step, we propose a framework that is an extension of the DVL algorithm, based on the knowledge obtained in the systematic review and our analysis of the DVL. This framework results in an algorithm for MaOPs recommended for situations that exist restrictions on the number of evaluations in the objective function.
Palavras-chave:	Computação Aprendizado do computador Linguagem unificada de modelagem Unified modeling language (UML) Multi-objective optimization Machine learning Inverse models
área CNPQ:	CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO
Idioma:	eng
Sigla da Instituição:	Universidade Federal de Sergipe
Programa de Pós-graduação:	Pós-Graduação em Ciência da Computação
Citação:	OLIVEIRA, Artur Leandro da Costa. A framework for inverse modeling applied to multi-objective evolutionary algorithms. 2022. 144 f. Dissertação (Mestrado em Ciência da Computação) – Universidade Federal de Sergipe, São Cristóvão, 2022.
URI:	http://ri.ufs.br/jspui/handle/riufs/16597
Aparece nas coleções:	Mestrado em Ciência da Computação

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