Memetic self-adaptive evolution strategies applied to the maximum diversity problem.

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.creatorFreitas, Alan Robert Resende de-
Autor(es): dc.creatorGuimarães, Frederico Gadelha-
Autor(es): dc.creatorSilva, Rodrigo César Pedrosa-
Autor(es): dc.creatorSouza, Marcone Jamilson Freitas-
Data de aceite: dc.date.accessioned2025-08-21T15:43:37Z-
Data de disponibilização: dc.date.available2025-08-21T15:43:37Z-
Data de envio: dc.date.issued2017-02-21-
Data de envio: dc.date.issued2017-02-21-
Data de envio: dc.date.issued2014-
Fonte completa do material: dc.identifierhttp://www.repositorio.ufop.br/handle/123456789/7290-
Fonte completa do material: dc.identifierhttp://link.springer.com/article/10.1007/s11590-013-0610-0-
Fonte completa do material: dc.identifierhttps://doi.org/10.1007/s11590-013-0610-0-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/capes/1022628-
Descrição: dc.descriptionThe maximum diversity problem consists in finding a subset of elements which have maximum diversity between each other. It is a very important problem due to its general aspect, that implies many practical applications such as facility location, genetics, and product design. We propose a method based on evolution strategies with local search and self-adaptation of the parameters. For all time limits from 1 to 300 s as well as for time to converge to the best solutions known, this method leads to better results when compared to other state-of-the-art algorithms.-
Formato: dc.formatapplication/pdf-
Idioma: dc.languageen-
Direitos: dc.rightsrestrito-
Palavras-chave: dc.subjectMetaheuristics-
Palavras-chave: dc.subjectEvolutionary algorithms-
Título: dc.titleMemetic self-adaptive evolution strategies applied to the maximum diversity problem.-
Aparece nas coleções:Repositório Institucional - UFOP

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