Performance and parameterization of the algorithm Simplified Generalized Simulated Annealing
Autor(a) principal: | |
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Data de Publicação: | 2004 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UnB |
Texto Completo: | http://repositorio.unb.br/handle/10482/26286 https://dx.doi.org/10.1590/S1415-47572004000400024 |
Resumo: | The main goal of this study is to find the most effective set of parameters for the Simplified Generalized Simulated Annealing algorithm, SGSA, when applied to distinct cost function as well as to find a possible correlation between the values of these parameters sets and some topological characteristics of the hypersurface of the respective cost function. The SGSA algorithm is an extended and simplified derivative of the GSA algorithm, a Markovian stochastic process based on Tsallis statistics that has been used in many classes of problems, in particular, in biological molecular systems optimization. In all but one of the studied cost functions, the global minimum was found in 100% of the 50 runs. For these functions the best visiting parameter, qV, belongs to the interval [1.2, 1.7]. Also, the temperature decaying parameter, qT, should be increased when better precision is required. Moreover, the similarity in the locus of optimal parameter sets observed in some functions indicates that possibly one could extract topological information about the cost functions from these sets. |
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Performance and parameterization of the algorithm Simplified Generalized Simulated AnnealingOtimizaçãoRecozimento simulado generalizadoThe main goal of this study is to find the most effective set of parameters for the Simplified Generalized Simulated Annealing algorithm, SGSA, when applied to distinct cost function as well as to find a possible correlation between the values of these parameters sets and some topological characteristics of the hypersurface of the respective cost function. The SGSA algorithm is an extended and simplified derivative of the GSA algorithm, a Markovian stochastic process based on Tsallis statistics that has been used in many classes of problems, in particular, in biological molecular systems optimization. In all but one of the studied cost functions, the global minimum was found in 100% of the 50 runs. For these functions the best visiting parameter, qV, belongs to the interval [1.2, 1.7]. Also, the temperature decaying parameter, qT, should be increased when better precision is required. Moreover, the similarity in the locus of optimal parameter sets observed in some functions indicates that possibly one could extract topological information about the cost functions from these sets.Em processamentoSociedade Brasileira de Genética2017-12-07T04:41:35Z2017-12-07T04:41:35Z2004info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfGenet. Mol. Biol.,v.27,n.4,p.616-622,2004http://repositorio.unb.br/handle/10482/26286https://dx.doi.org/10.1590/S1415-47572004000400024Dall'Igna Júnior, AlcinoSilva, Renato S.Mundim, Kleber C.Dardenne, Laurent E.info:eu-repo/semantics/openAccessengreponame:Repositório Institucional da UnBinstname:Universidade de Brasília (UnB)instacron:UNB2024-08-28T19:04:10Zoai:repositorio.unb.br:10482/26286Repositório InstitucionalPUBhttps://repositorio.unb.br/oai/requestrepositorio@unb.bropendoar:2024-08-28T19:04:10Repositório Institucional da UnB - Universidade de Brasília (UnB)false |
dc.title.none.fl_str_mv |
Performance and parameterization of the algorithm Simplified Generalized Simulated Annealing |
title |
Performance and parameterization of the algorithm Simplified Generalized Simulated Annealing |
spellingShingle |
Performance and parameterization of the algorithm Simplified Generalized Simulated Annealing Dall'Igna Júnior, Alcino Otimização Recozimento simulado generalizado |
title_short |
Performance and parameterization of the algorithm Simplified Generalized Simulated Annealing |
title_full |
Performance and parameterization of the algorithm Simplified Generalized Simulated Annealing |
title_fullStr |
Performance and parameterization of the algorithm Simplified Generalized Simulated Annealing |
title_full_unstemmed |
Performance and parameterization of the algorithm Simplified Generalized Simulated Annealing |
title_sort |
Performance and parameterization of the algorithm Simplified Generalized Simulated Annealing |
author |
Dall'Igna Júnior, Alcino |
author_facet |
Dall'Igna Júnior, Alcino Silva, Renato S. Mundim, Kleber C. Dardenne, Laurent E. |
author_role |
author |
author2 |
Silva, Renato S. Mundim, Kleber C. Dardenne, Laurent E. |
author2_role |
author author author |
dc.contributor.author.fl_str_mv |
Dall'Igna Júnior, Alcino Silva, Renato S. Mundim, Kleber C. Dardenne, Laurent E. |
dc.subject.por.fl_str_mv |
Otimização Recozimento simulado generalizado |
topic |
Otimização Recozimento simulado generalizado |
description |
The main goal of this study is to find the most effective set of parameters for the Simplified Generalized Simulated Annealing algorithm, SGSA, when applied to distinct cost function as well as to find a possible correlation between the values of these parameters sets and some topological characteristics of the hypersurface of the respective cost function. The SGSA algorithm is an extended and simplified derivative of the GSA algorithm, a Markovian stochastic process based on Tsallis statistics that has been used in many classes of problems, in particular, in biological molecular systems optimization. In all but one of the studied cost functions, the global minimum was found in 100% of the 50 runs. For these functions the best visiting parameter, qV, belongs to the interval [1.2, 1.7]. Also, the temperature decaying parameter, qT, should be increased when better precision is required. Moreover, the similarity in the locus of optimal parameter sets observed in some functions indicates that possibly one could extract topological information about the cost functions from these sets. |
publishDate |
2004 |
dc.date.none.fl_str_mv |
2004 2017-12-07T04:41:35Z 2017-12-07T04:41:35Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
Genet. Mol. Biol.,v.27,n.4,p.616-622,2004 http://repositorio.unb.br/handle/10482/26286 https://dx.doi.org/10.1590/S1415-47572004000400024 |
identifier_str_mv |
Genet. Mol. Biol.,v.27,n.4,p.616-622,2004 |
url |
http://repositorio.unb.br/handle/10482/26286 https://dx.doi.org/10.1590/S1415-47572004000400024 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Sociedade Brasileira de Genética |
publisher.none.fl_str_mv |
Sociedade Brasileira de Genética |
dc.source.none.fl_str_mv |
reponame:Repositório Institucional da UnB instname:Universidade de Brasília (UnB) instacron:UNB |
instname_str |
Universidade de Brasília (UnB) |
instacron_str |
UNB |
institution |
UNB |
reponame_str |
Repositório Institucional da UnB |
collection |
Repositório Institucional da UnB |
repository.name.fl_str_mv |
Repositório Institucional da UnB - Universidade de Brasília (UnB) |
repository.mail.fl_str_mv |
repositorio@unb.br |
_version_ |
1810580724250574848 |