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
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/24826
Ver los metadatos del registro completo
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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 Objeto de conferencia http://purl.org/coar/resource_type/c_5794 info:ar-repo/semantics/documentoDeConferencia |
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http://sedici.unlp.edu.ar/handle/10915/24826 |
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http://sedici.unlp.edu.ar/handle/10915/24826 |
dc.language.none.fl_str_mv |
eng |
language |
eng |
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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) |
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openAccess |
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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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