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1. Identificação
Tipo de ReferênciaArtigo em Revista Científica (Journal Article)
Sitemtc-m21c.sid.inpe.br
Código do Detentorisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identificador8JMKD3MGP3W34R/43F85D2
Repositóriosid.inpe.br/mtc-m21c/2020/10.23.12.00   (acesso restrito)
Última Atualização2020:10.23.12.00.29 (UTC) simone
Repositório de Metadadossid.inpe.br/mtc-m21c/2020/10.23.12.00.29
Última Atualização dos Metadados2022:01.04.01.35.29 (UTC) administrator
DOI10.1016/j.asoc.2020.106760
ISSN1568-4946
1872-9681
Chave de CitaçãoSantiagoJúniorÖzcaCarv:2020:HyBaRe
TítuloHyper-Heuristics based on reinforcement learning, balanced heuristic selection and group decision acceptance
Ano2020
MêsDec.
Data de Acesso28 mar. 2024
Tipo de Trabalhojournal article
Tipo SecundárioPRE PI
Número de Arquivos1
Tamanho2234 KiB
2. Contextualização
Autor1 Santiago Júnior, Valdivino Alexandre de
2 Özcan, Ender
3 Carvalho, Vinicius Renan de
Identificador de Curriculo1 8JMKD3MGP5W/3C9JJB5
Grupo1 LABAC-COCTE-INPE-MCTIC-GOV-BR
Afiliação1 Instituto Nacional de Pesquisas Espaciais (INPE)
2 University of Nottingham
3 Universidade de São Paulo (USP)
Endereço de e-Mail do Autor1 valdivino.santiago@inpe.br
2 Ender.Ozcan@nottingham.ac.uk
3 vrcarvalho@usp.br
RevistaApplied Soft Computing Journal
Volume97
Páginase106760
Nota SecundáriaA2_INTERDISCIPLINAR A2_ENGENHARIAS_IV A2_ENGENHARIAS_III A2_CIÊNCIA_DA_COMPUTAÇÃO B1_MATEMÁTICA_/_PROBABILIDADE_E_ESTATÍSTICA B1_ENGENHARIAS_II B1_BIOTECNOLOGIA
Histórico (UTC)2020-10-23 12:00:29 :: simone -> administrator ::
2020-10-23 12:00:30 :: administrator -> simone :: 2020
2020-10-23 12:00:51 :: simone -> administrator :: 2020
2020-10-24 10:46:16 :: administrator -> simone :: 2020
2020-12-14 11:54:54 :: simone -> administrator :: 2020
2022-01-04 01:35:29 :: administrator -> simone :: 2020
3. Conteúdo e estrutura
É a matriz ou uma cópia?é a matriz
Estágio do Conteúdoconcluido
Transferível1
Tipo do ConteúdoExternal Contribution
Tipo de Versãopublisher
Palavras-ChaveHyper-heuristic
Reinforcement learning
Balanced heuristic selection
Group decision-making
Multi-objective evolutionary algorithms
Multi-objective optimisation
ResumoIn this paper, we introduce a multi-objective selection hyper-heuristic approach combining Reinforcement Learning, (meta)heuristic selection, and group decision-making as acceptance methods, referred to as Hyper-Heuristic based on Reinforcement LearnIng, Balanced Heuristic Selection and Group Decision AccEptance (HRISE), controlling a set of Multi-Objective Evolutionary Algorithms (MOEAs) as Low-Level (meta)Heuristics (LLHs). Along with the use of multiple MOEAs, we believe that having a robust LLH selection method as well as several move acceptance methods at our disposal would lead to an improved general-purpose method producing most adequate solutions to the problem instances across multiple domains. We present two learning hyper-heuristics based on the HRISE framework for multi-objective optimisation, each embedding a group decision-making acceptance method under a different rule: majority rule (HRISE_M) and responsibility rule (HRISE_R). A third hyper-heuristic is also defined where both a random LLH selection and a random move acceptance strategy are used. We also propose two variants of the late acceptance method and a new quality indicator supporting the initialisation of selection hyper-heuristics using low computational budget. An extensive set of experiments were performed using 39 multi-objective problem instances from various domains where 24 are from four different benchmark function classes, and the remaining 15 instances are from four different real-world problems. The cross-domain search performance of the proposed learning hyperheuristics indeed turned out to be the best, particularly HRISE_R, when compared to three other selection hyper-heuristics, including a recently proposed one, and all low-level MOEAs each run in isolation.
ÁreaCOMP
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4. Condições de acesso e uso
Idiomaen
Arquivo Alvovaldivino_hyper.pdf
Grupo de Usuáriossimone
Grupo de Leitoresadministrator
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Visibilidadeshown
Política de Arquivamentodenypublisher denyfinaldraft24
Permissão de Leituradeny from all and allow from 150.163
Permissão de Atualizaçãonão transferida
5. Fontes relacionadas
Unidades Imediatamente Superiores8JMKD3MGPCW/3ESGTTP
DivulgaçãoWEBSCI; PORTALCAPES; COMPENDEX.
Acervo Hospedeirourlib.net/www/2017/11.22.19.04
6. Notas
Campos Vaziosalternatejournal archivist callnumber copyholder copyright creatorhistory descriptionlevel e-mailaddress format isbn label lineage mark mirrorrepository nextedition notes number orcid parameterlist parentrepositories previousedition previouslowerunit progress project rightsholder schedulinginformation secondarydate secondarykey session shorttitle sponsor subject tertiarymark tertiarytype url
7. Controle da descrição
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