Harmony Search-Based Approaches for Fine-Tuning Deep Belief Networks

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorUniversity of Wolverhampton-
Autor(es): dc.contributorGachon University-
Autor(es): dc.creatorRodrigues, Douglas-
Autor(es): dc.creatorRoder, Mateus-
Autor(es): dc.creatorPassos, Leandro Aparecido-
Autor(es): dc.creatorRosa, Gustavo Henrique de-
Autor(es): dc.creatorPapa, João Paulo-
Autor(es): dc.creatorGeem, Zong Woo-
Data de aceite: dc.date.accessioned2025-08-21T21:35:05Z-
Data de disponibilização: dc.date.available2025-08-21T21:35:05Z-
Data de envio: dc.date.issued2023-07-29-
Data de envio: dc.date.issued2023-07-29-
Data de envio: dc.date.issued2022-12-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1007/978-3-031-22371-6_5-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/249857-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/249857-
Descrição: dc.descriptionHarmony Search (HS) is a metaheuristic algorithm inspired by the musical composition process, precisely the composition of harmonies, i.e., the chain of different musical notes. The algorithm’s simplicity allows several points to improve to explore the entire search space efficiently. This work aims to compare different HS variants in image restoration using Deep Belief Networks (DBN). We compared standard HS against five variants: Improved Harmony Search (IHS), Self-adaptive Global Best Harmony Search (SGHS), Global-best Harmony Search (GHS), Novel Global Harmony Search (NGHS), and Global Harmony Search with Generalized Opposition-based learning (GOGHS). Experiments in public datasets for binary image reconstruction highlighted that HS and its variants obtained superior results than a random search used as a baseline. Also, it was found that the GHS variant is inferior to the others for some cases.-
Descrição: dc.descriptionDepartment of Computing São Paulo State University-
Descrição: dc.descriptionSchool of Engineering and Informatics University of Wolverhampton-
Descrição: dc.descriptionDepartment of Energy IT Gachon University-
Descrição: dc.descriptionDepartment of Computing São Paulo State University-
Formato: dc.format105-118-
Idioma: dc.languageen-
Relação: dc.relationIntelligent Systems Reference Library-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectDeep Belief Networks-
Palavras-chave: dc.subjectHarmony Search-
Palavras-chave: dc.subjectMetaheuristic optimization-
Título: dc.titleHarmony Search-Based Approaches for Fine-Tuning Deep Belief Networks-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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