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

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dc.contributor.authorRodrigues, Douglas [UNESP]
dc.contributor.authorRoder, Mateus [UNESP]
dc.contributor.authorPassos, Leandro Aparecido
dc.contributor.authorRosa, Gustavo Henrique de [UNESP]
dc.contributor.authorPapa, João Paulo [UNESP]
dc.contributor.authorGeem, Zong Woo
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)
dc.contributor.institutionUniversity of Wolverhampton
dc.contributor.institutionGachon University
dc.date.accessioned2023-07-29T16:11:04Z
dc.date.available2023-07-29T16:11:04Z
dc.date.issued2023-01-01
dc.description.abstractHarmony 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.en
dc.description.affiliationDepartment of Computing São Paulo State University
dc.description.affiliationSchool of Engineering and Informatics University of Wolverhampton
dc.description.affiliationDepartment of Energy IT Gachon University
dc.description.affiliationUnespDepartment of Computing São Paulo State University
dc.format.extent105-118
dc.identifierhttp://dx.doi.org/10.1007/978-3-031-22371-6_5
dc.identifier.citationIntelligent Systems Reference Library, v. 236, p. 105-118.
dc.identifier.doi10.1007/978-3-031-22371-6_5
dc.identifier.issn1868-4408
dc.identifier.issn1868-4394
dc.identifier.scopus2-s2.0-85152447198
dc.identifier.urihttp://hdl.handle.net/11449/249857
dc.language.isoeng
dc.relation.ispartofIntelligent Systems Reference Library
dc.sourceScopus
dc.subjectDeep Belief Networks
dc.subjectHarmony Search
dc.subjectMetaheuristic optimization
dc.titleHarmony Search-Based Approaches for Fine-Tuning Deep Belief Networksen
dc.typeCapítulo de livro
unesp.campusUniversidade Estadual Paulista (Unesp), Faculdade de Ciências, Baurupt
unesp.departmentComputação - FCpt

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