Evolving neural arrays: a new mechanism for learning complex action sequences
- Autores
- Corbalán, Leonardo César; Lanzarini, Laura Cristina
- Año de publicación
- 2017
- Idioma
- inglés
- Tipo de recurso
- artículo
- Estado
- versión publicada
- Descripción
- Incremental evolution has proved to be an extremely useful mechanism in complex actions sequence learning. Its performance is based on the decomposition of the original problem into increasingly complex stages whose learning is carried out sequentially, starting from the simplest stage and thus increasing its generality and difficulty. The present work proposes neural array applications as a novel mechanism for complex actions sequence learning. Each array is composed by several neural nets obtained by means of an evolving process allowing them to acquire various degrees of specialization. Neural nets constituting the same array are organized so that, in each assessment, there is only one in charge of its response. The proposed strategy is applied to problems presented by obstacle evasion and target reaching as a means to show the capability of this proposal to solve complex problems. The measurements carried out show the superiority of evolving neural arrays over traditional neuroevolving methods that handle neural network populations – SANE is being particularly used as a comparative reference due to its high performance. Neural array capability to recover from previous defective evolving stages has been tested, evincing highly plausible final successful outcomes – even in those adverse cases. Finally, conclusions are presented as well as some future lines of work.
Instituto de Investigación en Informática - Materia
-
Ciencias Informáticas
Evolving neural nets
Learning
Complex actions sequence learning
Incremental evolution
Genetic algorithms - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- http://creativecommons.org/licenses/by-nc-sa/4.0/
- Repositorio
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/103776
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Evolving neural arrays: a new mechanism for learning complex action sequencesCorbalán, Leonardo CésarLanzarini, Laura CristinaCiencias InformáticasEvolving neural netsLearningComplex actions sequence learningIncremental evolutionGenetic algorithmsIncremental evolution has proved to be an extremely useful mechanism in complex actions sequence learning. Its performance is based on the decomposition of the original problem into increasingly complex stages whose learning is carried out sequentially, starting from the simplest stage and thus increasing its generality and difficulty. The present work proposes neural array applications as a novel mechanism for complex actions sequence learning. Each array is composed by several neural nets obtained by means of an evolving process allowing them to acquire various degrees of specialization. Neural nets constituting the same array are organized so that, in each assessment, there is only one in charge of its response. The proposed strategy is applied to problems presented by obstacle evasion and target reaching as a means to show the capability of this proposal to solve complex problems. The measurements carried out show the superiority of evolving neural arrays over traditional neuroevolving methods that handle neural network populations – SANE is being particularly used as a comparative reference due to its high performance. Neural array capability to recover from previous defective evolving stages has been tested, evincing highly plausible final successful outcomes – even in those adverse cases. Finally, conclusions are presented as well as some future lines of work.Instituto de Investigación en Informática2017info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArticulohttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttp://sedici.unlp.edu.ar/handle/10915/103776enginfo:eu-repo/semantics/altIdentifier/url/http://www.clei.org/cleiej/index.php/cleiej/article/view/348info:eu-repo/semantics/altIdentifier/issn/0717-5000info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-29T11:22:33Zoai:sedici.unlp.edu.ar:10915/103776Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 11:22:34.229SEDICI (UNLP) - Universidad Nacional de La Platafalse |
dc.title.none.fl_str_mv |
Evolving neural arrays: a new mechanism for learning complex action sequences |
title |
Evolving neural arrays: a new mechanism for learning complex action sequences |
spellingShingle |
Evolving neural arrays: a new mechanism for learning complex action sequences Corbalán, Leonardo César Ciencias Informáticas Evolving neural nets Learning Complex actions sequence learning Incremental evolution Genetic algorithms |
title_short |
Evolving neural arrays: a new mechanism for learning complex action sequences |
title_full |
Evolving neural arrays: a new mechanism for learning complex action sequences |
title_fullStr |
Evolving neural arrays: a new mechanism for learning complex action sequences |
title_full_unstemmed |
Evolving neural arrays: a new mechanism for learning complex action sequences |
title_sort |
Evolving neural arrays: a new mechanism for learning complex action sequences |
dc.creator.none.fl_str_mv |
Corbalán, Leonardo César Lanzarini, Laura Cristina |
author |
Corbalán, Leonardo César |
author_facet |
Corbalán, Leonardo César Lanzarini, Laura Cristina |
author_role |
author |
author2 |
Lanzarini, Laura Cristina |
author2_role |
author |
dc.subject.none.fl_str_mv |
Ciencias Informáticas Evolving neural nets Learning Complex actions sequence learning Incremental evolution Genetic algorithms |
topic |
Ciencias Informáticas Evolving neural nets Learning Complex actions sequence learning Incremental evolution Genetic algorithms |
dc.description.none.fl_txt_mv |
Incremental evolution has proved to be an extremely useful mechanism in complex actions sequence learning. Its performance is based on the decomposition of the original problem into increasingly complex stages whose learning is carried out sequentially, starting from the simplest stage and thus increasing its generality and difficulty. The present work proposes neural array applications as a novel mechanism for complex actions sequence learning. Each array is composed by several neural nets obtained by means of an evolving process allowing them to acquire various degrees of specialization. Neural nets constituting the same array are organized so that, in each assessment, there is only one in charge of its response. The proposed strategy is applied to problems presented by obstacle evasion and target reaching as a means to show the capability of this proposal to solve complex problems. The measurements carried out show the superiority of evolving neural arrays over traditional neuroevolving methods that handle neural network populations – SANE is being particularly used as a comparative reference due to its high performance. Neural array capability to recover from previous defective evolving stages has been tested, evincing highly plausible final successful outcomes – even in those adverse cases. Finally, conclusions are presented as well as some future lines of work. Instituto de Investigación en Informática |
description |
Incremental evolution has proved to be an extremely useful mechanism in complex actions sequence learning. Its performance is based on the decomposition of the original problem into increasingly complex stages whose learning is carried out sequentially, starting from the simplest stage and thus increasing its generality and difficulty. The present work proposes neural array applications as a novel mechanism for complex actions sequence learning. Each array is composed by several neural nets obtained by means of an evolving process allowing them to acquire various degrees of specialization. Neural nets constituting the same array are organized so that, in each assessment, there is only one in charge of its response. The proposed strategy is applied to problems presented by obstacle evasion and target reaching as a means to show the capability of this proposal to solve complex problems. The measurements carried out show the superiority of evolving neural arrays over traditional neuroevolving methods that handle neural network populations – SANE is being particularly used as a comparative reference due to its high performance. Neural array capability to recover from previous defective evolving stages has been tested, evincing highly plausible final successful outcomes – even in those adverse cases. Finally, conclusions are presented as well as some future lines of work. |
publishDate |
2017 |
dc.date.none.fl_str_mv |
2017 |
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 |
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article |
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publishedVersion |
dc.identifier.none.fl_str_mv |
http://sedici.unlp.edu.ar/handle/10915/103776 |
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http://sedici.unlp.edu.ar/handle/10915/103776 |
dc.language.none.fl_str_mv |
eng |
language |
eng |
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info:eu-repo/semantics/altIdentifier/url/http://www.clei.org/cleiej/index.php/cleiej/article/view/348 info:eu-repo/semantics/altIdentifier/issn/0717-5000 |
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info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by-nc-sa/4.0/ Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) |
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openAccess |
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http://creativecommons.org/licenses/by-nc-sa/4.0/ Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) |
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