A comparison of fitness scallng methods in evolutionary algorithms

Autores
Bertone, E.; Alfonso, Hugo; Gallard, Raúl Hector
Año de publicación
1999
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
Proportional selection (PS), as a selection mechanism for mating (reproduction with emphasis), selects individuals according to their fitness. Consequently the probability of an individual to obtain a number of offspring is directly proportional to its fitness value. This can lead to a loss of selective pressure in the fmal stages of the evolutionary process degrading the search. This presentation discusses performance results on evolutionary algorithms optimizing two highly multimodal (Michalewicz's and Griewank's) functions and a hard unimodal (Easom' s) function. Experiments were addressed to contrast the behaviour of a simple genetic algorithm against three scaling methods: linear, sigma truncation and recency-weighted- running-average. Diverse measures of performance were used to establish quality of results and convergence speed.
Eje: Redes y sistemas inteligentes
Red de Universidades con Carreras en Informática (RedUNCI)
Materia
Ciencias Informáticas
ARTIFICIAL INTELLIGENCE
Algorithms
methods in evolutionary algorithms
comparison of fitness
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:10915/22219
Acceder
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network_name_str SEDICI (UNLP)
spelling A comparison of fitness scallng methods in evolutionary algorithmsBertone, E.Alfonso, HugoGallard, Raúl HectorCiencias InformáticasARTIFICIAL INTELLIGENCEAlgorithmsmethods in evolutionary algorithmscomparison of fitnessProportional selection (PS), as a selection mechanism for mating (reproduction with emphasis), selects individuals according to their fitness. Consequently the probability of an individual to obtain a number of offspring is directly proportional to its fitness value. This can lead to a loss of selective pressure in the fmal stages of the evolutionary process degrading the search. This presentation discusses performance results on evolutionary algorithms optimizing two highly multimodal (Michalewicz's and Griewank's) functions and a hard unimodal (Easom' s) function. Experiments were addressed to contrast the behaviour of a simple genetic algorithm against three scaling methods: linear, sigma truncation and recency-weighted- running-average. Diverse measures of performance were used to establish quality of results and convergence speed.Eje: Redes y sistemas inteligentesRed de Universidades con Carreras en Informática (RedUNCI)1999-05info: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/10915/22219enginfo: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:UNLP2025-10-15T10:47:37Zoai:sedici.unlp.edu.ar:10915/22219Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-10-15 10:47:37.392SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv A comparison of fitness scallng methods in evolutionary algorithms
title A comparison of fitness scallng methods in evolutionary algorithms
spellingShingle A comparison of fitness scallng methods in evolutionary algorithms
Bertone, E.
Ciencias Informáticas
ARTIFICIAL INTELLIGENCE
Algorithms
methods in evolutionary algorithms
comparison of fitness
title_short A comparison of fitness scallng methods in evolutionary algorithms
title_full A comparison of fitness scallng methods in evolutionary algorithms
title_fullStr A comparison of fitness scallng methods in evolutionary algorithms
title_full_unstemmed A comparison of fitness scallng methods in evolutionary algorithms
title_sort A comparison of fitness scallng methods in evolutionary algorithms
dc.creator.none.fl_str_mv Bertone, E.
Alfonso, Hugo
Gallard, Raúl Hector
author Bertone, E.
author_facet Bertone, E.
Alfonso, Hugo
Gallard, Raúl Hector
author_role author
author2 Alfonso, Hugo
Gallard, Raúl Hector
author2_role author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
ARTIFICIAL INTELLIGENCE
Algorithms
methods in evolutionary algorithms
comparison of fitness
topic Ciencias Informáticas
ARTIFICIAL INTELLIGENCE
Algorithms
methods in evolutionary algorithms
comparison of fitness
dc.description.none.fl_txt_mv Proportional selection (PS), as a selection mechanism for mating (reproduction with emphasis), selects individuals according to their fitness. Consequently the probability of an individual to obtain a number of offspring is directly proportional to its fitness value. This can lead to a loss of selective pressure in the fmal stages of the evolutionary process degrading the search. This presentation discusses performance results on evolutionary algorithms optimizing two highly multimodal (Michalewicz's and Griewank's) functions and a hard unimodal (Easom' s) function. Experiments were addressed to contrast the behaviour of a simple genetic algorithm against three scaling methods: linear, sigma truncation and recency-weighted- running-average. Diverse measures of performance were used to establish quality of results and convergence speed.
Eje: Redes y sistemas inteligentes
Red de Universidades con Carreras en Informática (RedUNCI)
description Proportional selection (PS), as a selection mechanism for mating (reproduction with emphasis), selects individuals according to their fitness. Consequently the probability of an individual to obtain a number of offspring is directly proportional to its fitness value. This can lead to a loss of selective pressure in the fmal stages of the evolutionary process degrading the search. This presentation discusses performance results on evolutionary algorithms optimizing two highly multimodal (Michalewicz's and Griewank's) functions and a hard unimodal (Easom' s) function. Experiments were addressed to contrast the behaviour of a simple genetic algorithm against three scaling methods: linear, sigma truncation and recency-weighted- running-average. Diverse measures of performance were used to establish quality of results and convergence speed.
publishDate 1999
dc.date.none.fl_str_mv 1999-05
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
info:eu-repo/semantics/publishedVersion
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status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/22219
url http://sedici.unlp.edu.ar/handle/10915/22219
dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
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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
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reponame_str SEDICI (UNLP)
collection SEDICI (UNLP)
instname_str Universidad Nacional de La Plata
instacron_str UNLP
institution UNLP
repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
repository.mail.fl_str_mv alira@sedici.unlp.edu.ar
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