1. Identificação | |
Tipo de Referência | Artigo em Revista Científica (Journal Article) |
Site | mtc-m21c.sid.inpe.br |
Código do Detentor | isadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S |
Identificador | 8JMKD3MGP3W34R/43F85D2 |
Repositório | sid.inpe.br/mtc-m21c/2020/10.23.12.00 (acesso restrito) |
Última Atualização | 2020:10.23.12.00.29 (UTC) simone |
Repositório de Metadados | sid.inpe.br/mtc-m21c/2020/10.23.12.00.29 |
Última Atualização dos Metadados | 2022:01.04.01.35.29 (UTC) administrator |
DOI | 10.1016/j.asoc.2020.106760 |
ISSN | 1568-4946 1872-9681 |
Chave de Citação | SantiagoJúniorÖzcaCarv:2020:HyBaRe |
Título | Hyper-Heuristics based on reinforcement learning, balanced heuristic selection and group decision acceptance |
Ano | 2020 |
Mês | Dec. |
Data de Acesso | 23 maio 2024 |
Tipo de Trabalho | journal article |
Tipo Secundário | PRE PI |
Número de Arquivos | 1 |
Tamanho | 2234 KiB |
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2. Contextualização | |
Autor | 1 Santiago Júnior, Valdivino Alexandre de 2 Özcan, Ender 3 Carvalho, Vinicius Renan de |
Identificador de Curriculo | 1 8JMKD3MGP5W/3C9JJB5 |
Grupo | 1 LABAC-COCTE-INPE-MCTIC-GOV-BR |
Afiliação | 1 Instituto Nacional de Pesquisas Espaciais (INPE) 2 University of Nottingham 3 Universidade de São Paulo (USP) |
Endereço de e-Mail do Autor | 1 valdivino.santiago@inpe.br 2 Ender.Ozcan@nottingham.ac.uk 3 vrcarvalho@usp.br |
Revista | Applied Soft Computing Journal |
Volume | 97 |
Páginas | e106760 |
Nota Secundária | A2_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 |
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3. Conteúdo e estrutura | |
É a matriz ou uma cópia? | é a matriz |
Estágio do Conteúdo | concluido |
Transferível | 1 |
Tipo do Conteúdo | External Contribution |
Tipo de Versão | publisher |
Palavras-Chave | Hyper-heuristic Reinforcement learning Balanced heuristic selection Group decision-making Multi-objective evolutionary algorithms Multi-objective optimisation |
Resumo | In 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. |
Área | COMP |
Arranjo | urlib.net > BDMCI > Fonds > Produção anterior à 2021 > LABAC > Hyper-Heuristics based on... |
Conteúdo da Pasta doc | acessar |
Conteúdo da Pasta source | não têm arquivos |
Conteúdo da Pasta agreement | |
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4. Condições de acesso e uso | |
Idioma | en |
Arquivo Alvo | valdivino_hyper.pdf |
Grupo de Usuários | simone |
Grupo de Leitores | administrator simone |
Visibilidade | shown |
Política de Arquivamento | denypublisher denyfinaldraft24 |
Permissão de Leitura | deny from all and allow from 150.163 |
Permissão de Atualização | não transferida |
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5. Fontes relacionadas | |
Unidades Imediatamente Superiores | 8JMKD3MGPCW/3ESGTTP |
Lista de Itens Citando | sid.inpe.br/bibdigital/2013/09.22.23.14 4 sid.inpe.br/mtc-m21/2012/07.13.15.01.24 2 |
Divulgação | WEBSCI; PORTALCAPES; COMPENDEX. |
Acervo Hospedeiro | urlib.net/www/2017/11.22.19.04 |
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6. Notas | |
Campos Vazios | alternatejournal 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 |
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7. Controle da descrição | |
e-Mail (login) | simone |
atualizar | |
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