| dc.contributor | Carvalho, André Britto de | |
| dc.contributor | Gusmão, Renê Pereira de | |
| dc.creator | Oliveira, Artur Leandro da Costa | |
| dc.date | 2022-10-10T13:49:10Z | |
| dc.date | 2022-10-10T13:49:10Z | |
| dc.date | 2022-06-08 | |
| dc.date.accessioned | 2023-09-28T23:01:57Z | |
| dc.date.available | 2023-09-28T23:01:57Z | |
| dc.identifier | 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. | |
| dc.identifier | http://ri.ufs.br/jspui/handle/riufs/16597 | |
| dc.identifier.uri | https://repositorioslatinoamericanos.uchile.cl/handle/2250/9082905 | |
| dc.description | 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. | |
| dc.description | São Cristóvão | |
| dc.format | application/pdf | |
| dc.language | eng | |
| dc.publisher | Pós-Graduação em Ciência da Computação | |
| dc.publisher | Universidade Federal de Sergipe | |
| dc.subject | Computação | |
| dc.subject | Aprendizado do computador | |
| dc.subject | Linguagem unificada de modelagem | |
| dc.subject | Unified modeling language (UML) | |
| dc.subject | Multi-objective optimization | |
| dc.subject | Machine learning | |
| dc.subject | Inverse models | |
| dc.subject | CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO | |
| dc.title | A framework for inverse modeling applied to multi-objective evolutionary algorithms | |
| dc.type | Dissertação | |
