A New Frequency Analysis Operator for Population Improvement in Genetic Algorithms to Solve the Job Shop Scheduling Problem†

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Autor(es): dc.contributorUniversidade Federal de São Carlos (UFSCar)-
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorUniversidade de São Paulo (USP)-
Autor(es): dc.creatorViana, Monique Simplicio-
Autor(es): dc.creatorContreras, Rodrigo Colnago-
Autor(es): dc.creatorJunior, Orides Morandin-
Data de aceite: dc.date.accessioned2025-08-21T18:25:20Z-
Data de disponibilização: dc.date.available2025-08-21T18:25:20Z-
Data de envio: dc.date.issued2023-03-01-
Data de envio: dc.date.issued2023-03-01-
Data de envio: dc.date.issued2022-06-01-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.3390/s22124561-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/240354-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/240354-
Descrição: dc.descriptionJob Shop Scheduling is currently one of the most addressed planning and scheduling optimization problems in the field. Due to its complexity, as it belongs to the NP-Hard class of problems, meta-heuristics are one of the most commonly used approaches in its resolution, with Genetic Algorithms being one of the most effective methods in this category. However, it is well known that this meta-heuristic is affected by phenomena that worsen the quality of its population, such as premature convergence and population concentration in regions of local optima. To circumvent these difficulties, we propose, in this work, the use of a guidance operator responsible for modifying ill-adapted individuals using genetic material from well-adapted individuals. We also propose, in this paper, a new method of determining the genetic quality of individuals using genetic frequency analysis. Our method is evaluated over a wide range of modern GAs and considers two case studies defined by well-established JSSP benchmarks in the literature. The results show that the use of the proposed operator assists in managing individuals with poor fitness values, which improves the population quality of the algorithms and, consequently, leads to obtaining better results in the solution of JSSP instances. Finally, the use of the proposed operator in the most elaborate GA-like method in the literature was able to reduce its mean relative error from 1.395% to 0.755%, representing an improvement of 45.88%.-
Descrição: dc.descriptionCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)-
Descrição: dc.descriptionDepartment of Computing Federal University of Sao Carlos, SP-
Descrição: dc.descriptionDepartment of Computer Science and Statistics Institute of Biosciences Letters and Exact Sciences Sao Paulo State University, SP-
Descrição: dc.descriptionDepartment of Applied Mathematics and Statistics Institute of Mathematical and Computer Science University of Sao Paulo, SP-
Descrição: dc.descriptionDepartment of Computer Science and Statistics Institute of Biosciences Letters and Exact Sciences Sao Paulo State University, SP-
Idioma: dc.languageen-
Relação: dc.relationSensors-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectcombinatorial optimization-
Palavras-chave: dc.subjectevolutionary algorithm-
Palavras-chave: dc.subjectgenetic algorithm-
Palavras-chave: dc.subjectgenetic improvement-
Palavras-chave: dc.subjectjob shop scheduling problem-
Título: dc.titleA New Frequency Analysis Operator for Population Improvement in Genetic Algorithms to Solve the Job Shop Scheduling Problem†-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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