Multi-PBil: an estimation distribution algorithm applied to multimodal optimization problems
- Autores
- Barbosa Filho, Rubens
- Año de publicación
- 2006
- Idioma
- inglés
- Tipo de recurso
- documento de conferencia
- Estado
- versión publicada
- Descripción
- The Estimation Distribution Algorithms (EDAs) compose an evolutionary metaheuristic whose main characteristic is the construction of solutions in randomly form, using a distribution of probabilities that evolves during the execution. The Population-Based Incremental Learning Algorithm (PBIL) is a type of EDA where the variables are independent, that is, they do not have significant interactions between themselves. The PBIL considers that the solutions can be represented as vectors of discrete variables, what makes it more adequate for combinatorial optimization problems. This paper presents a method called Multi-PBil that is an extension of PBIL with applications in multimodal problems. The Multi-PBil was developed with the goal to have an efficient and non expensive algorithm of search in multimodal spaces. From PBIL, it was implemented a routine that allows the Multi-PBil to create a probability model to act in the search space. A formula that allows initiating the probability models in regions of the search space next to the searched global points was applied in the process of the probability model initialization rule. The Multi-PBil method was tested and analyzed, presenting some experimental results that highlight its viability and characteristics. It is also shown a comparison of the performance between the Multi-PBil and a traditional Genetic Algorithm using the sharing method.
VII Workshop de Agentes y Sistemas Inteligentes (WASI)
Red de Universidades con Carreras en Informática (RedUNCI) - Materia
-
Ciencias Informáticas
Inteligencia Artificial
Algorithms
Algoritmos
evolutionary computation
genetic algorithms
estimation distribution algorithms - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
- Repositorio
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/22663
Ver los metadatos del registro completo
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Multi-PBil: an estimation distribution algorithm applied to multimodal optimization problemsBarbosa Filho, RubensCiencias InformáticasInteligencia ArtificialAlgorithmsAlgoritmosevolutionary computationgenetic algorithmsestimation distribution algorithmsThe Estimation Distribution Algorithms (EDAs) compose an evolutionary metaheuristic whose main characteristic is the construction of solutions in randomly form, using a distribution of probabilities that evolves during the execution. The Population-Based Incremental Learning Algorithm (PBIL) is a type of EDA where the variables are independent, that is, they do not have significant interactions between themselves. The PBIL considers that the solutions can be represented as vectors of discrete variables, what makes it more adequate for combinatorial optimization problems. This paper presents a method called Multi-PBil that is an extension of PBIL with applications in multimodal problems. The Multi-PBil was developed with the goal to have an efficient and non expensive algorithm of search in multimodal spaces. From PBIL, it was implemented a routine that allows the Multi-PBil to create a probability model to act in the search space. A formula that allows initiating the probability models in regions of the search space next to the searched global points was applied in the process of the probability model initialization rule. The Multi-PBil method was tested and analyzed, presenting some experimental results that highlight its viability and characteristics. It is also shown a comparison of the performance between the Multi-PBil and a traditional Genetic Algorithm using the sharing method.VII Workshop de Agentes y Sistemas Inteligentes (WASI)Red de Universidades con Carreras en Informática (RedUNCI)2006-10info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdf1267-1278http://sedici.unlp.edu.ar/handle/10915/22663enginfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/2.5/ar/Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-10-15T10:47:46Zoai:sedici.unlp.edu.ar:10915/22663Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-10-15 10:47:46.975SEDICI (UNLP) - Universidad Nacional de La Platafalse |
dc.title.none.fl_str_mv |
Multi-PBil: an estimation distribution algorithm applied to multimodal optimization problems |
title |
Multi-PBil: an estimation distribution algorithm applied to multimodal optimization problems |
spellingShingle |
Multi-PBil: an estimation distribution algorithm applied to multimodal optimization problems Barbosa Filho, Rubens Ciencias Informáticas Inteligencia Artificial Algorithms Algoritmos evolutionary computation genetic algorithms estimation distribution algorithms |
title_short |
Multi-PBil: an estimation distribution algorithm applied to multimodal optimization problems |
title_full |
Multi-PBil: an estimation distribution algorithm applied to multimodal optimization problems |
title_fullStr |
Multi-PBil: an estimation distribution algorithm applied to multimodal optimization problems |
title_full_unstemmed |
Multi-PBil: an estimation distribution algorithm applied to multimodal optimization problems |
title_sort |
Multi-PBil: an estimation distribution algorithm applied to multimodal optimization problems |
dc.creator.none.fl_str_mv |
Barbosa Filho, Rubens |
author |
Barbosa Filho, Rubens |
author_facet |
Barbosa Filho, Rubens |
author_role |
author |
dc.subject.none.fl_str_mv |
Ciencias Informáticas Inteligencia Artificial Algorithms Algoritmos evolutionary computation genetic algorithms estimation distribution algorithms |
topic |
Ciencias Informáticas Inteligencia Artificial Algorithms Algoritmos evolutionary computation genetic algorithms estimation distribution algorithms |
dc.description.none.fl_txt_mv |
The Estimation Distribution Algorithms (EDAs) compose an evolutionary metaheuristic whose main characteristic is the construction of solutions in randomly form, using a distribution of probabilities that evolves during the execution. The Population-Based Incremental Learning Algorithm (PBIL) is a type of EDA where the variables are independent, that is, they do not have significant interactions between themselves. The PBIL considers that the solutions can be represented as vectors of discrete variables, what makes it more adequate for combinatorial optimization problems. This paper presents a method called Multi-PBil that is an extension of PBIL with applications in multimodal problems. The Multi-PBil was developed with the goal to have an efficient and non expensive algorithm of search in multimodal spaces. From PBIL, it was implemented a routine that allows the Multi-PBil to create a probability model to act in the search space. A formula that allows initiating the probability models in regions of the search space next to the searched global points was applied in the process of the probability model initialization rule. The Multi-PBil method was tested and analyzed, presenting some experimental results that highlight its viability and characteristics. It is also shown a comparison of the performance between the Multi-PBil and a traditional Genetic Algorithm using the sharing method. VII Workshop de Agentes y Sistemas Inteligentes (WASI) Red de Universidades con Carreras en Informática (RedUNCI) |
description |
The Estimation Distribution Algorithms (EDAs) compose an evolutionary metaheuristic whose main characteristic is the construction of solutions in randomly form, using a distribution of probabilities that evolves during the execution. The Population-Based Incremental Learning Algorithm (PBIL) is a type of EDA where the variables are independent, that is, they do not have significant interactions between themselves. The PBIL considers that the solutions can be represented as vectors of discrete variables, what makes it more adequate for combinatorial optimization problems. This paper presents a method called Multi-PBil that is an extension of PBIL with applications in multimodal problems. The Multi-PBil was developed with the goal to have an efficient and non expensive algorithm of search in multimodal spaces. From PBIL, it was implemented a routine that allows the Multi-PBil to create a probability model to act in the search space. A formula that allows initiating the probability models in regions of the search space next to the searched global points was applied in the process of the probability model initialization rule. The Multi-PBil method was tested and analyzed, presenting some experimental results that highlight its viability and characteristics. It is also shown a comparison of the performance between the Multi-PBil and a traditional Genetic Algorithm using the sharing method. |
publishDate |
2006 |
dc.date.none.fl_str_mv |
2006-10 |
dc.type.none.fl_str_mv |
info:eu-repo/semantics/conferenceObject info:eu-repo/semantics/publishedVersion Objeto de conferencia http://purl.org/coar/resource_type/c_5794 info:ar-repo/semantics/documentoDeConferencia |
format |
conferenceObject |
status_str |
publishedVersion |
dc.identifier.none.fl_str_mv |
http://sedici.unlp.edu.ar/handle/10915/22663 |
url |
http://sedici.unlp.edu.ar/handle/10915/22663 |
dc.language.none.fl_str_mv |
eng |
language |
eng |
dc.rights.none.fl_str_mv |
info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by-nc-sa/2.5/ar/ Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5) |
eu_rights_str_mv |
openAccess |
rights_invalid_str_mv |
http://creativecommons.org/licenses/by-nc-sa/2.5/ar/ Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5) |
dc.format.none.fl_str_mv |
application/pdf 1267-1278 |
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reponame:SEDICI (UNLP) instname:Universidad Nacional de La Plata instacron:UNLP |
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Universidad Nacional de La Plata |
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SEDICI (UNLP) - Universidad Nacional de La Plata |
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13.22299 |