A framework for inverse modeling applied to multi-objective evolutionary algorithms

Detalhes bibliográficos
Autor(a) principal: Oliveira, Artur Leandro da Costa
Data de Publicação: 2022
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Institucional da UFS
Texto Completo: http://ri.ufs.br/jspui/handle/riufs/16597
Resumo: São Cristóvão
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spelling Oliveira, Artur Leandro da CostaCarvalho, André Britto deGusmão, Renê Pereira de2022-10-10T13:49:10Z2022-10-10T13:49:10Z2022-06-08OLIVEIRA, 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.http://ri.ufs.br/jspui/handle/riufs/16597engComputaçãoAprendizado do computadorLinguagem unificada de modelagemUnified modeling language (UML)Multi-objective optimizationMachine learningInverse modelsCIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAOA framework for inverse modeling applied to multi-objective evolutionary algorithmsinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisSão CristóvãoMany-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.Pós-Graduação em Ciência da ComputaçãoUniversidade Federal de Sergipereponame:Repositório Institucional da UFSinstname:Universidade Federal de Sergipe (UFS)instacron:UFSinfo:eu-repo/semantics/openAccessLICENSElicense.txtlicense.txttext/plain; charset=utf-81475https://ri.ufs.br/jspui/bitstream/riufs/16597/1/license.txt098cbbf65c2c15e1fb2e49c5d306a44cMD51ORIGINALARTUR_LEANDRO_COSTA_OLIVEIRA.pdfARTUR_LEANDRO_COSTA_OLIVEIRA.pdfapplication/pdf3803205https://ri.ufs.br/jspui/bitstream/riufs/16597/2/ARTUR_LEANDRO_COSTA_OLIVEIRA.pdf88fb504fa06c04b30f224c003f3863e7MD52TEXTARTUR_LEANDRO_COSTA_OLIVEIRA.pdf.txtARTUR_LEANDRO_COSTA_OLIVEIRA.pdf.txtExtracted texttext/plain390699https://ri.ufs.br/jspui/bitstream/riufs/16597/3/ARTUR_LEANDRO_COSTA_OLIVEIRA.pdf.txt6e2836890351be701ac14d2f104f5d15MD53THUMBNAILARTUR_LEANDRO_COSTA_OLIVEIRA.pdf.jpgARTUR_LEANDRO_COSTA_OLIVEIRA.pdf.jpgGenerated Thumbnailimage/jpeg1448https://ri.ufs.br/jspui/bitstream/riufs/16597/4/ARTUR_LEANDRO_COSTA_OLIVEIRA.pdf.jpg8d1ef16350f94493c7166144281f3223MD54riufs/165972022-10-10 10:49:11.09oai:ufs.br:riufs/16597TElDRU7Dh0EgREUgRElTVFJJQlVJw4fDg08gTsODTy1FWENMVVNJVkEKCkNvbSBhIGFwcmVzZW50YcOnw6NvIGRlc3RhIGxpY2Vuw6dhLCB2b2PDqiAobyBhdXRvcihlcykgb3UgbyB0aXR1bGFyIGRvcyBkaXJlaXRvcyBkZSBhdXRvcikgY29uY2VkZSDDoCBVbml2ZXJzaWRhZGUgRmVkZXJhbCBkZSBTZXJnaXBlIG8gZGlyZWl0byBuw6NvLWV4Y2x1c2l2byBkZSByZXByb2R1emlyIHNldSB0cmFiYWxobyBubyBmb3JtYXRvIGVsZXRyw7RuaWNvLCBpbmNsdWluZG8gb3MgZm9ybWF0b3Mgw6F1ZGlvIG91IHbDrWRlby4KClZvY8OqIGNvbmNvcmRhIHF1ZSBhIFVuaXZlcnNpZGFkZSBGZWRlcmFsIGRlIFNlcmdpcGUgcG9kZSwgc2VtIGFsdGVyYXIgbyBjb250ZcO6ZG8sIHRyYW5zcG9yIHNldSB0cmFiYWxobyBwYXJhIHF1YWxxdWVyIG1laW8gb3UgZm9ybWF0byBwYXJhIGZpbnMgZGUgcHJlc2VydmHDp8Ojby4KClZvY8OqIHRhbWLDqW0gY29uY29yZGEgcXVlIGEgVW5pdmVyc2lkYWRlIEZlZGVyYWwgZGUgU2VyZ2lwZSBwb2RlIG1hbnRlciBtYWlzIGRlIHVtYSBjw7NwaWEgZGUgc2V1IHRyYWJhbGhvIHBhcmEgZmlucyBkZSBzZWd1cmFuw6dhLCBiYWNrLXVwIGUgcHJlc2VydmHDp8Ojby4KClZvY8OqIGRlY2xhcmEgcXVlIHNldSB0cmFiYWxobyDDqSBvcmlnaW5hbCBlIHF1ZSB2b2PDqiB0ZW0gbyBwb2RlciBkZSBjb25jZWRlciBvcyBkaXJlaXRvcyBjb250aWRvcyBuZXN0YSBsaWNlbsOnYS4gVm9jw6ogdGFtYsOpbSBkZWNsYXJhIHF1ZSBvIGRlcMOzc2l0bywgcXVlIHNlamEgZGUgc2V1IGNvbmhlY2ltZW50bywgbsOjbyBpbmZyaW5nZSBkaXJlaXRvcyBhdXRvcmFpcyBkZSBuaW5ndcOpbS4KCkNhc28gbyB0cmFiYWxobyBjb250ZW5oYSBtYXRlcmlhbCBxdWUgdm9jw6ogbsOjbyBwb3NzdWkgYSB0aXR1bGFyaWRhZGUgZG9zIGRpcmVpdG9zIGF1dG9yYWlzLCB2b2PDqiBkZWNsYXJhIHF1ZSBvYnRldmUgYSBwZXJtaXNzw6NvIGlycmVzdHJpdGEgZG8gZGV0ZW50b3IgZG9zIGRpcmVpdG9zIGF1dG9yYWlzIHBhcmEgY29uY2VkZXIgw6AgVW5pdmVyc2lkYWRlIEZlZGVyYWwgZGUgU2VyZ2lwZSBvcyBkaXJlaXRvcyBhcHJlc2VudGFkb3MgbmVzdGEgbGljZW7Dp2EsIGUgcXVlIGVzc2UgbWF0ZXJpYWwgZGUgcHJvcHJpZWRhZGUgZGUgdGVyY2Vpcm9zIGVzdMOhIGNsYXJhbWVudGUgaWRlbnRpZmljYWRvIGUgcmVjb25oZWNpZG8gbm8gdGV4dG8gb3Ugbm8gY29udGXDumRvLgoKQSBVbml2ZXJzaWRhZGUgRmVkZXJhbCBkZSBTZXJnaXBlIHNlIGNvbXByb21ldGUgYSBpZGVudGlmaWNhciBjbGFyYW1lbnRlIG8gc2V1IG5vbWUocykgb3UgbyhzKSBub21lKHMpIGRvKHMpIApkZXRlbnRvcihlcykgZG9zIGRpcmVpdG9zIGF1dG9yYWlzIGRvIHRyYWJhbGhvLCBlIG7Do28gZmFyw6EgcXVhbHF1ZXIgYWx0ZXJhw6fDo28sIGFsw6ltIGRhcXVlbGFzIGNvbmNlZGlkYXMgcG9yIGVzdGEgbGljZW7Dp2EuIAo=Repositório InstitucionalPUBhttps://ri.ufs.br/oai/requestrepositorio@academico.ufs.bropendoar:2022-10-10T13:49:11Repositório Institucional da UFS - Universidade Federal de Sergipe (UFS)false
dc.title.pt_BR.fl_str_mv A framework for inverse modeling applied to multi-objective evolutionary algorithms
title A framework for inverse modeling applied to multi-objective evolutionary algorithms
spellingShingle A framework for inverse modeling applied to multi-objective evolutionary algorithms
Oliveira, Artur Leandro da Costa
Computação
Aprendizado do computador
Linguagem unificada de modelagem
Unified modeling language (UML)
Multi-objective optimization
Machine learning
Inverse models
CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO
title_short A framework for inverse modeling applied to multi-objective evolutionary algorithms
title_full A framework for inverse modeling applied to multi-objective evolutionary algorithms
title_fullStr A framework for inverse modeling applied to multi-objective evolutionary algorithms
title_full_unstemmed A framework for inverse modeling applied to multi-objective evolutionary algorithms
title_sort A framework for inverse modeling applied to multi-objective evolutionary algorithms
author Oliveira, Artur Leandro da Costa
author_facet Oliveira, Artur Leandro da Costa
author_role author
dc.contributor.author.fl_str_mv Oliveira, Artur Leandro da Costa
dc.contributor.advisor1.fl_str_mv Carvalho, André Britto de
dc.contributor.advisor-co1.fl_str_mv Gusmão, Renê Pereira de
contributor_str_mv Carvalho, André Britto de
Gusmão, Renê Pereira de
dc.subject.por.fl_str_mv Computação
Aprendizado do computador
Linguagem unificada de modelagem
topic Computação
Aprendizado do computador
Linguagem unificada de modelagem
Unified modeling language (UML)
Multi-objective optimization
Machine learning
Inverse models
CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO
dc.subject.eng.fl_str_mv Unified modeling language (UML)
Multi-objective optimization
Machine learning
Inverse models
dc.subject.cnpq.fl_str_mv CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO
description São Cristóvão
publishDate 2022
dc.date.accessioned.fl_str_mv 2022-10-10T13:49:10Z
dc.date.available.fl_str_mv 2022-10-10T13:49:10Z
dc.date.issued.fl_str_mv 2022-06-08
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
status_str publishedVersion
dc.identifier.citation.fl_str_mv 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.uri.fl_str_mv http://ri.ufs.br/jspui/handle/riufs/16597
identifier_str_mv 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.
url http://ri.ufs.br/jspui/handle/riufs/16597
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.program.fl_str_mv Pós-Graduação em Ciência da Computação
dc.publisher.initials.fl_str_mv Universidade Federal de Sergipe
dc.source.none.fl_str_mv reponame:Repositório Institucional da UFS
instname:Universidade Federal de Sergipe (UFS)
instacron:UFS
instname_str Universidade Federal de Sergipe (UFS)
instacron_str UFS
institution UFS
reponame_str Repositório Institucional da UFS
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