Alternative strategies for asynchronous migration-controlled schemes in parallel genetic algorithms

Autores
Ochoa, Claudio; Gallard, Raúl Hector
Año de publicación
1997
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
Migration of individuals allows a fruitful interaction between subpopulations in the island model, a well known distributed approach for evolutionary computing, where separate subpopulations evolve in parallel. This model is well suited for a distributed environment running a Single Program Multiple Data (SPMD) scheme. Here, the same Genetic Algorithm (GA) is replicated in many processors and attempting better convergence, through an expected improvement on genetic diversity, selected individuals are exchanged periodically. For exchanging, an individual is selected from a source subpopulation and then exported towards a target subpopulation. Usually, the imported string is accepted on arrival and then inserted into the target subpopulation. Our earlier experiments on controlled migration showed an improvement on results when contrasted against those obtained by conventional migration approaches. This paper describes extended implementations of alternative strategies to oversee migration in asynchronous schemes for an island model and enlarges a previous work on three processors with a set of softer testing functions [9]. All of them try to decrease the risk of premature convergence. A first strategy attempts to prevent unbalanced propagation of genotypes by applying an acceptance threshold parameter to each incoming string. A second one permits independent evolution of subpopulations and acts only when a possible stagnation is detected. In such condition an attempt to evade falling towards a local optimum is done by inserting an expected dissimilar individual to improve genetic diversity. A third alternative strategy combines both previous mentioned strategies. The results presented are those obtained on the functions that showed to be more difficult for the island model using a replication of a simple GA. A description of the corresponding system architecture supporting the PGA implementation is described and results for the parallel distributed approach among 3, 6 and 12 processors is discussed.
Eje: Procesamiento distribuido y paralelo. Tratamiento de señales
Red de Universidades con Carreras en Informática (RedUNCI)
Materia
Ciencias Informáticas
Parallel algorithms
Parallel processing
Distributed
Parallel genetic algorithms
island model
migration schemes
acceptance threshold
dynamic arbiter
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/23894

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oai_identifier_str oai:sedici.unlp.edu.ar:10915/23894
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network_name_str SEDICI (UNLP)
spelling Alternative strategies for asynchronous migration-controlled schemes in parallel genetic algorithmsOchoa, ClaudioGallard, Raúl HectorCiencias InformáticasParallel algorithmsParallel processingDistributedParallel genetic algorithmsisland modelmigration schemesacceptance thresholddynamic arbiterMigration of individuals allows a fruitful interaction between subpopulations in the island model, a well known distributed approach for evolutionary computing, where separate subpopulations evolve in parallel. This model is well suited for a distributed environment running a Single Program Multiple Data (SPMD) scheme. Here, the same Genetic Algorithm (GA) is replicated in many processors and attempting better convergence, through an expected improvement on genetic diversity, selected individuals are exchanged periodically. For exchanging, an individual is selected from a source subpopulation and then exported towards a target subpopulation. Usually, the imported string is accepted on arrival and then inserted into the target subpopulation. Our earlier experiments on controlled migration showed an improvement on results when contrasted against those obtained by conventional migration approaches. This paper describes extended implementations of alternative strategies to oversee migration in asynchronous schemes for an island model and enlarges a previous work on three processors with a set of softer testing functions [9]. All of them try to decrease the risk of premature convergence. A first strategy attempts to prevent unbalanced propagation of genotypes by applying an acceptance threshold parameter to each incoming string. A second one permits independent evolution of subpopulations and acts only when a possible stagnation is detected. In such condition an attempt to evade falling towards a local optimum is done by inserting an expected dissimilar individual to improve genetic diversity. A third alternative strategy combines both previous mentioned strategies. The results presented are those obtained on the functions that showed to be more difficult for the island model using a replication of a simple GA. A description of the corresponding system architecture supporting the PGA implementation is described and results for the parallel distributed approach among 3, 6 and 12 processors is discussed.Eje: Procesamiento distribuido y paralelo. Tratamiento de señalesRed de Universidades con Carreras en Informática (RedUNCI)1997info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdfhttp://sedici.unlp.edu.ar/handle/10915/23894enginfo: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:UNLP2025-09-03T10:28:25Zoai:sedici.unlp.edu.ar:10915/23894Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-03 10:28:25.452SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Alternative strategies for asynchronous migration-controlled schemes in parallel genetic algorithms
title Alternative strategies for asynchronous migration-controlled schemes in parallel genetic algorithms
spellingShingle Alternative strategies for asynchronous migration-controlled schemes in parallel genetic algorithms
Ochoa, Claudio
Ciencias Informáticas
Parallel algorithms
Parallel processing
Distributed
Parallel genetic algorithms
island model
migration schemes
acceptance threshold
dynamic arbiter
title_short Alternative strategies for asynchronous migration-controlled schemes in parallel genetic algorithms
title_full Alternative strategies for asynchronous migration-controlled schemes in parallel genetic algorithms
title_fullStr Alternative strategies for asynchronous migration-controlled schemes in parallel genetic algorithms
title_full_unstemmed Alternative strategies for asynchronous migration-controlled schemes in parallel genetic algorithms
title_sort Alternative strategies for asynchronous migration-controlled schemes in parallel genetic algorithms
dc.creator.none.fl_str_mv Ochoa, Claudio
Gallard, Raúl Hector
author Ochoa, Claudio
author_facet Ochoa, Claudio
Gallard, Raúl Hector
author_role author
author2 Gallard, Raúl Hector
author2_role author
dc.subject.none.fl_str_mv Ciencias Informáticas
Parallel algorithms
Parallel processing
Distributed
Parallel genetic algorithms
island model
migration schemes
acceptance threshold
dynamic arbiter
topic Ciencias Informáticas
Parallel algorithms
Parallel processing
Distributed
Parallel genetic algorithms
island model
migration schemes
acceptance threshold
dynamic arbiter
dc.description.none.fl_txt_mv Migration of individuals allows a fruitful interaction between subpopulations in the island model, a well known distributed approach for evolutionary computing, where separate subpopulations evolve in parallel. This model is well suited for a distributed environment running a Single Program Multiple Data (SPMD) scheme. Here, the same Genetic Algorithm (GA) is replicated in many processors and attempting better convergence, through an expected improvement on genetic diversity, selected individuals are exchanged periodically. For exchanging, an individual is selected from a source subpopulation and then exported towards a target subpopulation. Usually, the imported string is accepted on arrival and then inserted into the target subpopulation. Our earlier experiments on controlled migration showed an improvement on results when contrasted against those obtained by conventional migration approaches. This paper describes extended implementations of alternative strategies to oversee migration in asynchronous schemes for an island model and enlarges a previous work on three processors with a set of softer testing functions [9]. All of them try to decrease the risk of premature convergence. A first strategy attempts to prevent unbalanced propagation of genotypes by applying an acceptance threshold parameter to each incoming string. A second one permits independent evolution of subpopulations and acts only when a possible stagnation is detected. In such condition an attempt to evade falling towards a local optimum is done by inserting an expected dissimilar individual to improve genetic diversity. A third alternative strategy combines both previous mentioned strategies. The results presented are those obtained on the functions that showed to be more difficult for the island model using a replication of a simple GA. A description of the corresponding system architecture supporting the PGA implementation is described and results for the parallel distributed approach among 3, 6 and 12 processors is discussed.
Eje: Procesamiento distribuido y paralelo. Tratamiento de señales
Red de Universidades con Carreras en Informática (RedUNCI)
description Migration of individuals allows a fruitful interaction between subpopulations in the island model, a well known distributed approach for evolutionary computing, where separate subpopulations evolve in parallel. This model is well suited for a distributed environment running a Single Program Multiple Data (SPMD) scheme. Here, the same Genetic Algorithm (GA) is replicated in many processors and attempting better convergence, through an expected improvement on genetic diversity, selected individuals are exchanged periodically. For exchanging, an individual is selected from a source subpopulation and then exported towards a target subpopulation. Usually, the imported string is accepted on arrival and then inserted into the target subpopulation. Our earlier experiments on controlled migration showed an improvement on results when contrasted against those obtained by conventional migration approaches. This paper describes extended implementations of alternative strategies to oversee migration in asynchronous schemes for an island model and enlarges a previous work on three processors with a set of softer testing functions [9]. All of them try to decrease the risk of premature convergence. A first strategy attempts to prevent unbalanced propagation of genotypes by applying an acceptance threshold parameter to each incoming string. A second one permits independent evolution of subpopulations and acts only when a possible stagnation is detected. In such condition an attempt to evade falling towards a local optimum is done by inserting an expected dissimilar individual to improve genetic diversity. A third alternative strategy combines both previous mentioned strategies. The results presented are those obtained on the functions that showed to be more difficult for the island model using a replication of a simple GA. A description of the corresponding system architecture supporting the PGA implementation is described and results for the parallel distributed approach among 3, 6 and 12 processors is discussed.
publishDate 1997
dc.date.none.fl_str_mv 1997
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
info:eu-repo/semantics/publishedVersion
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dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/23894
url http://sedici.unlp.edu.ar/handle/10915/23894
dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
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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)
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