Global and partial function approximation with evolutionary algorithms

Autores
Kavka, Carlos; Roggero, Patricia; Schoenauer, Marc
Año de publicación
2001
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
We present an evolutionary algorithm that evolves a population of local approximators in order to fit an unknown function. The evolutionary algorithm performs a simultaneous learning of simple local approximators together with the regions in which the local approximators are applied. By combining these simple local approximators, the domain is in fact partitioned in the Voronoi diagram that has as centers, the center points of the region in which each local approximator is efficient and useful. Both continuous and non-continuous approaches are considered. The algorithm seems promising in order to develop good neural networks for function approximation.
Eje: Sistemas inteligentes
Red de Universidades con Carreras en Informática (RedUNCI)
Materia
Ciencias Informáticas
Evolutionary computation
Voronoi diagrams
Neural nets
Algorithms
ARTIFICIAL INTELLIGENCE
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:10915/23402
Acceder
id SEDICI_35ec40ea52b608d119843ce3517d3dd6
oai_identifier_str oai:sedici.unlp.edu.ar:10915/23402
network_acronym_str SEDICI
repository_id_str 1329
network_name_str SEDICI (UNLP)
spelling Global and partial function approximation with evolutionary algorithmsKavka, CarlosRoggero, PatriciaSchoenauer, MarcCiencias InformáticasEvolutionary computationVoronoi diagramsNeural netsAlgorithmsARTIFICIAL INTELLIGENCEWe present an evolutionary algorithm that evolves a population of local approximators in order to fit an unknown function. The evolutionary algorithm performs a simultaneous learning of simple local approximators together with the regions in which the local approximators are applied. By combining these simple local approximators, the domain is in fact partitioned in the Voronoi diagram that has as centers, the center points of the region in which each local approximator is efficient and useful. Both continuous and non-continuous approaches are considered. The algorithm seems promising in order to develop good neural networks for function approximation.Eje: Sistemas inteligentesRed de Universidades con Carreras en Informática (RedUNCI)2001-10info: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/10915/23402enginfo: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:UNLP2025-09-03T10:28:12Zoai:sedici.unlp.edu.ar:10915/23402Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-03 10:28:13.157SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Global and partial function approximation with evolutionary algorithms
title Global and partial function approximation with evolutionary algorithms
spellingShingle Global and partial function approximation with evolutionary algorithms
Kavka, Carlos
Ciencias Informáticas
Evolutionary computation
Voronoi diagrams
Neural nets
Algorithms
ARTIFICIAL INTELLIGENCE
title_short Global and partial function approximation with evolutionary algorithms
title_full Global and partial function approximation with evolutionary algorithms
title_fullStr Global and partial function approximation with evolutionary algorithms
title_full_unstemmed Global and partial function approximation with evolutionary algorithms
title_sort Global and partial function approximation with evolutionary algorithms
dc.creator.none.fl_str_mv Kavka, Carlos
Roggero, Patricia
Schoenauer, Marc
author Kavka, Carlos
author_facet Kavka, Carlos
Roggero, Patricia
Schoenauer, Marc
author_role author
author2 Roggero, Patricia
Schoenauer, Marc
author2_role author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
Evolutionary computation
Voronoi diagrams
Neural nets
Algorithms
ARTIFICIAL INTELLIGENCE
topic Ciencias Informáticas
Evolutionary computation
Voronoi diagrams
Neural nets
Algorithms
ARTIFICIAL INTELLIGENCE
dc.description.none.fl_txt_mv We present an evolutionary algorithm that evolves a population of local approximators in order to fit an unknown function. The evolutionary algorithm performs a simultaneous learning of simple local approximators together with the regions in which the local approximators are applied. By combining these simple local approximators, the domain is in fact partitioned in the Voronoi diagram that has as centers, the center points of the region in which each local approximator is efficient and useful. Both continuous and non-continuous approaches are considered. The algorithm seems promising in order to develop good neural networks for function approximation.
Eje: Sistemas inteligentes
Red de Universidades con Carreras en Informática (RedUNCI)
description We present an evolutionary algorithm that evolves a population of local approximators in order to fit an unknown function. The evolutionary algorithm performs a simultaneous learning of simple local approximators together with the regions in which the local approximators are applied. By combining these simple local approximators, the domain is in fact partitioned in the Voronoi diagram that has as centers, the center points of the region in which each local approximator is efficient and useful. Both continuous and non-continuous approaches are considered. The algorithm seems promising in order to develop good neural networks for function approximation.
publishDate 2001
dc.date.none.fl_str_mv 2001-10
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/10915/23402
url http://sedici.unlp.edu.ar/handle/10915/23402
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
dc.source.none.fl_str_mv reponame:SEDICI (UNLP)
instname:Universidad Nacional de La Plata
instacron:UNLP
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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score 13.13397

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