ALENA : Adaptive-Length Evolving Neural Arrays
- Autores
- Corbalán, Leonardo César; Lanzarini, Laura Cristina; De Giusti, Armando Eduardo
- Año de publicación
- 2004
- Idioma
- inglés
- Tipo de recurso
- artículo
- Estado
- versión publicada
- Descripción
- Evolving neural arrays (ENA) have proved to be capable of learning complex behaviors, i.e., problems whose solution requires strategy learning. For this reason, they present many applications in various areas such as robotics and process control. Unlike conventional methods "based on a single neural network" ENAs are made up of a set of networks organized as an array. Each of them represents a part of the expected solution. This work describes a new method, ALENA, that enhances the solutions obtained by solving the main deficiencies of ENA since it eases the obtaining of specialized components, does not require the explicit decomposition of the problem into subtasks, and is capable of automatically adjusting the arrays length for each particular use. The measurements of the proposed method "applied to problems of obstacle evasion and objects collection" show the superiority of ALENA in relation to the traditional methods that deal with populations of neural networks. SANE has been used in particular as a comparative referent due to its high performance. Eventually, conclusions and some future lines of work are presented.
Facultad de Informática - Materia
-
Ciencias Informáticas
genetic algorithm
Neural nets
Learning - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- http://creativecommons.org/licenses/by-nc/3.0/
- Repositorio
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/9481
Ver los metadatos del registro completo
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ALENA : Adaptive-Length Evolving Neural ArraysCorbalán, Leonardo CésarLanzarini, Laura CristinaDe Giusti, Armando EduardoCiencias Informáticasgenetic algorithmNeural netsLearningEvolving neural arrays (ENA) have proved to be capable of learning complex behaviors, i.e., problems whose solution requires strategy learning. For this reason, they present many applications in various areas such as robotics and process control. Unlike conventional methods "based on a single neural network" ENAs are made up of a set of networks organized as an array. Each of them represents a part of the expected solution. This work describes a new method, ALENA, that enhances the solutions obtained by solving the main deficiencies of ENA since it eases the obtaining of specialized components, does not require the explicit decomposition of the problem into subtasks, and is capable of automatically adjusting the arrays length for each particular use. The measurements of the proposed method "applied to problems of obstacle evasion and objects collection" show the superiority of ALENA in relation to the traditional methods that deal with populations of neural networks. SANE has been used in particular as a comparative referent due to its high performance. Eventually, conclusions and some future lines of work are presented.Facultad de Informática2004-04info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArticulohttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdf59-65http://sedici.unlp.edu.ar/handle/10915/9481enginfo:eu-repo/semantics/altIdentifier/url/http://journal.info.unlp.edu.ar/wp-content/uploads/JCST-Apr04-9.pdfinfo:eu-repo/semantics/altIdentifier/issn/1666-6038info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc/3.0/Creative Commons Attribution-NonCommercial 3.0 Unported (CC BY-NC 3.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-29T10:50:43Zoai:sedici.unlp.edu.ar:10915/9481Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 10:50:44.158SEDICI (UNLP) - Universidad Nacional de La Platafalse |
dc.title.none.fl_str_mv |
ALENA : Adaptive-Length Evolving Neural Arrays |
title |
ALENA : Adaptive-Length Evolving Neural Arrays |
spellingShingle |
ALENA : Adaptive-Length Evolving Neural Arrays Corbalán, Leonardo César Ciencias Informáticas genetic algorithm Neural nets Learning |
title_short |
ALENA : Adaptive-Length Evolving Neural Arrays |
title_full |
ALENA : Adaptive-Length Evolving Neural Arrays |
title_fullStr |
ALENA : Adaptive-Length Evolving Neural Arrays |
title_full_unstemmed |
ALENA : Adaptive-Length Evolving Neural Arrays |
title_sort |
ALENA : Adaptive-Length Evolving Neural Arrays |
dc.creator.none.fl_str_mv |
Corbalán, Leonardo César Lanzarini, Laura Cristina De Giusti, Armando Eduardo |
author |
Corbalán, Leonardo César |
author_facet |
Corbalán, Leonardo César Lanzarini, Laura Cristina De Giusti, Armando Eduardo |
author_role |
author |
author2 |
Lanzarini, Laura Cristina De Giusti, Armando Eduardo |
author2_role |
author author |
dc.subject.none.fl_str_mv |
Ciencias Informáticas genetic algorithm Neural nets Learning |
topic |
Ciencias Informáticas genetic algorithm Neural nets Learning |
dc.description.none.fl_txt_mv |
Evolving neural arrays (ENA) have proved to be capable of learning complex behaviors, i.e., problems whose solution requires strategy learning. For this reason, they present many applications in various areas such as robotics and process control. Unlike conventional methods "based on a single neural network" ENAs are made up of a set of networks organized as an array. Each of them represents a part of the expected solution. This work describes a new method, ALENA, that enhances the solutions obtained by solving the main deficiencies of ENA since it eases the obtaining of specialized components, does not require the explicit decomposition of the problem into subtasks, and is capable of automatically adjusting the arrays length for each particular use. The measurements of the proposed method "applied to problems of obstacle evasion and objects collection" show the superiority of ALENA in relation to the traditional methods that deal with populations of neural networks. SANE has been used in particular as a comparative referent due to its high performance. Eventually, conclusions and some future lines of work are presented. Facultad de Informática |
description |
Evolving neural arrays (ENA) have proved to be capable of learning complex behaviors, i.e., problems whose solution requires strategy learning. For this reason, they present many applications in various areas such as robotics and process control. Unlike conventional methods "based on a single neural network" ENAs are made up of a set of networks organized as an array. Each of them represents a part of the expected solution. This work describes a new method, ALENA, that enhances the solutions obtained by solving the main deficiencies of ENA since it eases the obtaining of specialized components, does not require the explicit decomposition of the problem into subtasks, and is capable of automatically adjusting the arrays length for each particular use. The measurements of the proposed method "applied to problems of obstacle evasion and objects collection" show the superiority of ALENA in relation to the traditional methods that deal with populations of neural networks. SANE has been used in particular as a comparative referent due to its high performance. Eventually, conclusions and some future lines of work are presented. |
publishDate |
2004 |
dc.date.none.fl_str_mv |
2004-04 |
dc.type.none.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Articulo http://purl.org/coar/resource_type/c_6501 info:ar-repo/semantics/articulo |
format |
article |
status_str |
publishedVersion |
dc.identifier.none.fl_str_mv |
http://sedici.unlp.edu.ar/handle/10915/9481 |
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http://sedici.unlp.edu.ar/handle/10915/9481 |
dc.language.none.fl_str_mv |
eng |
language |
eng |
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info:eu-repo/semantics/altIdentifier/url/http://journal.info.unlp.edu.ar/wp-content/uploads/JCST-Apr04-9.pdf info:eu-repo/semantics/altIdentifier/issn/1666-6038 |
dc.rights.none.fl_str_mv |
info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by-nc/3.0/ Creative Commons Attribution-NonCommercial 3.0 Unported (CC BY-NC 3.0) |
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openAccess |
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http://creativecommons.org/licenses/by-nc/3.0/ Creative Commons Attribution-NonCommercial 3.0 Unported (CC BY-NC 3.0) |
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application/pdf 59-65 |
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