Contrasting main selection methods in genetic algorithms

Autores
Alfonso, Hugo; Cesan, P.; Fernandez, Natalia; Minetti, Gabriela F.; Salto, Carolina; Velazco, L.; Gallard, Raúl Hector
Año de publicación
1998
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
In genetic algorithms selection mechanisms aim to favour reproduction of better individuals imposing a direction on the search process. It does not create new individuals; instead it selects comparatively good individuals from a population and typically does it according to their fitness. The idea is that interacting with other individuals (competition), those with higher fitness have a higher probability to be selected for mating. In that manner, because the fitness of an individual gives a measure of its "goodness", selection introduces the influence of the fitness function to the evolutionary process. Moreover, selection is the only operator of genetic algorithm where the fitness of an individual affects the evolution process. In such a process two important, strongly related, issues exist: selective pressure and population diversity. They are the sides of the same coin: exploitation of information gathered so far versus exploration of the searching space. Selection plays an important role here because strong selective pressure can lead to premature convergence and weak selective pressure can make the search ineffective [14]. Focussing on this equilibrium problem significant research has been done. In this work we introduce the main properties of selection, the usual selection mechanisms and finally show the effect of applying proportional, ranking and tournament selection to a set of well known multimodal testing functions on simple genetic algorithms. These are the most widely used selection mechanisms and each of them has their own features. A description of each method, experiment and statistical analyses of results under different parameter settings are reported.
Sistemas Inteligentes
Red de Universidades con Carreras en Informática (RedUNCI)
Materia
Ciencias Informáticas
Informática
genetic algorithms
selection mechanisms
genetic diversity
premature convergence
Biology and genetics
Algorithms
Combinatorial algorithms
Selection process
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:10915/24826

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spelling Contrasting main selection methods in genetic algorithmsAlfonso, HugoCesan, P.Fernandez, NataliaMinetti, Gabriela F.Salto, CarolinaVelazco, L.Gallard, Raúl HectorCiencias InformáticasInformáticagenetic algorithmsselection mechanismsgenetic diversitypremature convergenceBiology and geneticsAlgorithmsCombinatorial algorithmsSelection processIn genetic algorithms selection mechanisms aim to favour reproduction of better individuals imposing a direction on the search process. It does not create new individuals; instead it selects comparatively good individuals from a population and typically does it according to their fitness. The idea is that interacting with other individuals (competition), those with higher fitness have a higher probability to be selected for mating. In that manner, because the fitness of an individual gives a measure of its "goodness", selection introduces the influence of the fitness function to the evolutionary process. Moreover, selection is the only operator of genetic algorithm where the fitness of an individual affects the evolution process. In such a process two important, strongly related, issues exist: selective pressure and population diversity. They are the sides of the same coin: exploitation of information gathered so far versus exploration of the searching space. Selection plays an important role here because strong selective pressure can lead to premature convergence and weak selective pressure can make the search ineffective [14]. Focussing on this equilibrium problem significant research has been done. In this work we introduce the main properties of selection, the usual selection mechanisms and finally show the effect of applying proportional, ranking and tournament selection to a set of well known multimodal testing functions on simple genetic algorithms. These are the most widely used selection mechanisms and each of them has their own features. A description of each method, experiment and statistical analyses of results under different parameter settings are reported.Sistemas InteligentesRed de Universidades con Carreras en Informática (RedUNCI)1998-10info: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/10915/24826enginfo: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:UNLP2025-09-03T10:28:48Zoai:sedici.unlp.edu.ar:10915/24826Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-03 10:28:49.143SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Contrasting main selection methods in genetic algorithms
title Contrasting main selection methods in genetic algorithms
spellingShingle Contrasting main selection methods in genetic algorithms
Alfonso, Hugo
Ciencias Informáticas
Informática
genetic algorithms
selection mechanisms
genetic diversity
premature convergence
Biology and genetics
Algorithms
Combinatorial algorithms
Selection process
title_short Contrasting main selection methods in genetic algorithms
title_full Contrasting main selection methods in genetic algorithms
title_fullStr Contrasting main selection methods in genetic algorithms
title_full_unstemmed Contrasting main selection methods in genetic algorithms
title_sort Contrasting main selection methods in genetic algorithms
dc.creator.none.fl_str_mv Alfonso, Hugo
Cesan, P.
Fernandez, Natalia
Minetti, Gabriela F.
Salto, Carolina
Velazco, L.
Gallard, Raúl Hector
author Alfonso, Hugo
author_facet Alfonso, Hugo
Cesan, P.
Fernandez, Natalia
Minetti, Gabriela F.
Salto, Carolina
Velazco, L.
Gallard, Raúl Hector
author_role author
author2 Cesan, P.
Fernandez, Natalia
Minetti, Gabriela F.
Salto, Carolina
Velazco, L.
Gallard, Raúl Hector
author2_role author
author
author
author
author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
Informática
genetic algorithms
selection mechanisms
genetic diversity
premature convergence
Biology and genetics
Algorithms
Combinatorial algorithms
Selection process
topic Ciencias Informáticas
Informática
genetic algorithms
selection mechanisms
genetic diversity
premature convergence
Biology and genetics
Algorithms
Combinatorial algorithms
Selection process
dc.description.none.fl_txt_mv In genetic algorithms selection mechanisms aim to favour reproduction of better individuals imposing a direction on the search process. It does not create new individuals; instead it selects comparatively good individuals from a population and typically does it according to their fitness. The idea is that interacting with other individuals (competition), those with higher fitness have a higher probability to be selected for mating. In that manner, because the fitness of an individual gives a measure of its "goodness", selection introduces the influence of the fitness function to the evolutionary process. Moreover, selection is the only operator of genetic algorithm where the fitness of an individual affects the evolution process. In such a process two important, strongly related, issues exist: selective pressure and population diversity. They are the sides of the same coin: exploitation of information gathered so far versus exploration of the searching space. Selection plays an important role here because strong selective pressure can lead to premature convergence and weak selective pressure can make the search ineffective [14]. Focussing on this equilibrium problem significant research has been done. In this work we introduce the main properties of selection, the usual selection mechanisms and finally show the effect of applying proportional, ranking and tournament selection to a set of well known multimodal testing functions on simple genetic algorithms. These are the most widely used selection mechanisms and each of them has their own features. A description of each method, experiment and statistical analyses of results under different parameter settings are reported.
Sistemas Inteligentes
Red de Universidades con Carreras en Informática (RedUNCI)
description In genetic algorithms selection mechanisms aim to favour reproduction of better individuals imposing a direction on the search process. It does not create new individuals; instead it selects comparatively good individuals from a population and typically does it according to their fitness. The idea is that interacting with other individuals (competition), those with higher fitness have a higher probability to be selected for mating. In that manner, because the fitness of an individual gives a measure of its "goodness", selection introduces the influence of the fitness function to the evolutionary process. Moreover, selection is the only operator of genetic algorithm where the fitness of an individual affects the evolution process. In such a process two important, strongly related, issues exist: selective pressure and population diversity. They are the sides of the same coin: exploitation of information gathered so far versus exploration of the searching space. Selection plays an important role here because strong selective pressure can lead to premature convergence and weak selective pressure can make the search ineffective [14]. Focussing on this equilibrium problem significant research has been done. In this work we introduce the main properties of selection, the usual selection mechanisms and finally show the effect of applying proportional, ranking and tournament selection to a set of well known multimodal testing functions on simple genetic algorithms. These are the most widely used selection mechanisms and each of them has their own features. A description of each method, experiment and statistical analyses of results under different parameter settings are reported.
publishDate 1998
dc.date.none.fl_str_mv 1998-10
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
info:eu-repo/semantics/publishedVersion
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dc.language.none.fl_str_mv eng
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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)
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