Harmony Search-Based Approaches for Fine-Tuning Deep Belief Networks
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Outros Autores: | , , , , |
Tipo de documento: | Capítulo de livro |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1007/978-3-031-22371-6_5 http://hdl.handle.net/11449/249857 |
Resumo: | Harmony 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. |
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Repositório Institucional da UNESP |
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Harmony Search-Based Approaches for Fine-Tuning Deep Belief NetworksDeep Belief NetworksHarmony SearchMetaheuristic optimizationHarmony 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.Department of Computing São Paulo State UniversitySchool of Engineering and Informatics University of WolverhamptonDepartment of Energy IT Gachon UniversityDepartment of Computing São Paulo State UniversityUniversidade Estadual Paulista (UNESP)University of WolverhamptonGachon UniversityRodrigues, Douglas [UNESP]Roder, Mateus [UNESP]Passos, Leandro AparecidoRosa, Gustavo Henrique de [UNESP]Papa, João Paulo [UNESP]Geem, Zong Woo2023-07-29T16:11:04Z2023-07-29T16:11:04Z2023-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/bookPart105-118http://dx.doi.org/10.1007/978-3-031-22371-6_5Intelligent Systems Reference Library, v. 236, p. 105-118.1868-44081868-4394http://hdl.handle.net/11449/24985710.1007/978-3-031-22371-6_52-s2.0-85152447198Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengIntelligent Systems Reference Libraryinfo:eu-repo/semantics/openAccess2024-04-23T16:11:01Zoai:repositorio.unesp.br:11449/249857Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T21:45:16.665431Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Harmony Search-Based Approaches for Fine-Tuning Deep Belief Networks |
title |
Harmony Search-Based Approaches for Fine-Tuning Deep Belief Networks |
spellingShingle |
Harmony Search-Based Approaches for Fine-Tuning Deep Belief Networks Rodrigues, Douglas [UNESP] Deep Belief Networks Harmony Search Metaheuristic optimization |
title_short |
Harmony Search-Based Approaches for Fine-Tuning Deep Belief Networks |
title_full |
Harmony Search-Based Approaches for Fine-Tuning Deep Belief Networks |
title_fullStr |
Harmony Search-Based Approaches for Fine-Tuning Deep Belief Networks |
title_full_unstemmed |
Harmony Search-Based Approaches for Fine-Tuning Deep Belief Networks |
title_sort |
Harmony Search-Based Approaches for Fine-Tuning Deep Belief Networks |
author |
Rodrigues, Douglas [UNESP] |
author_facet |
Rodrigues, Douglas [UNESP] Roder, Mateus [UNESP] Passos, Leandro Aparecido Rosa, Gustavo Henrique de [UNESP] Papa, João Paulo [UNESP] Geem, Zong Woo |
author_role |
author |
author2 |
Roder, Mateus [UNESP] Passos, Leandro Aparecido Rosa, Gustavo Henrique de [UNESP] Papa, João Paulo [UNESP] Geem, Zong Woo |
author2_role |
author author author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (UNESP) University of Wolverhampton Gachon University |
dc.contributor.author.fl_str_mv |
Rodrigues, Douglas [UNESP] Roder, Mateus [UNESP] Passos, Leandro Aparecido Rosa, Gustavo Henrique de [UNESP] Papa, João Paulo [UNESP] Geem, Zong Woo |
dc.subject.por.fl_str_mv |
Deep Belief Networks Harmony Search Metaheuristic optimization |
topic |
Deep Belief Networks Harmony Search Metaheuristic optimization |
description |
Harmony 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. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-07-29T16:11:04Z 2023-07-29T16:11:04Z 2023-01-01 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/bookPart |
format |
bookPart |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1007/978-3-031-22371-6_5 Intelligent Systems Reference Library, v. 236, p. 105-118. 1868-4408 1868-4394 http://hdl.handle.net/11449/249857 10.1007/978-3-031-22371-6_5 2-s2.0-85152447198 |
url |
http://dx.doi.org/10.1007/978-3-031-22371-6_5 http://hdl.handle.net/11449/249857 |
identifier_str_mv |
Intelligent Systems Reference Library, v. 236, p. 105-118. 1868-4408 1868-4394 10.1007/978-3-031-22371-6_5 2-s2.0-85152447198 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Intelligent Systems Reference Library |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
105-118 |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1808129354587176960 |