Cyclic evolution: a new strategy for improving controllers obtained by layered evolution
- Autores
- Olivera, Javier Hugo; Lanzarini, Laura Cristina
- Año de publicación
- 2005
- Idioma
- inglés
- Tipo de recurso
- artículo
- Estado
- versión publicada
- Descripción
- Complex control tasks may be solved by dividing them into a more specific and more easily handled subtasks hierarchy. Several authors have demonstrated that the incremental layered evolution paradigm allows obtaining controllers capable of solving this type of tasks. In this direction, different solutions combining Incremental Evolution with Evolving Neural Networks have been developed in order to provide an adaptive mechanism minimizing the previous knowledge necessary to obtain a good performance giving place to controllers made up of several networks. This paper is focused on the presentation of a new mechanism, called Cyclic Evolution, which allows improving controllers based on neural networks obtained through layered evolution. Its performance is based on continuing the cyclic improvement of each of the networks making up the controller within the whole domain of the problem. The proposed method of this paper has been used to solve the Keepaway game with successful results compared to other solutions recently proposed. Finally, some conclusions are included together with some future lines of work.
Facultad de Informática - Materia
-
Ciencias Informáticas
Neural nets
incremental evolution
layered evolution - 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:10915/9595
Ver los metadatos del registro completo
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Cyclic evolution: a new strategy for improving controllers obtained by layered evolutionOlivera, Javier HugoLanzarini, Laura CristinaCiencias InformáticasNeural netsincremental evolutionlayered evolutionComplex control tasks may be solved by dividing them into a more specific and more easily handled subtasks hierarchy. Several authors have demonstrated that the incremental layered evolution paradigm allows obtaining controllers capable of solving this type of tasks. In this direction, different solutions combining Incremental Evolution with Evolving Neural Networks have been developed in order to provide an adaptive mechanism minimizing the previous knowledge necessary to obtain a good performance giving place to controllers made up of several networks. This paper is focused on the presentation of a new mechanism, called Cyclic Evolution, which allows improving controllers based on neural networks obtained through layered evolution. Its performance is based on continuing the cyclic improvement of each of the networks making up the controller within the whole domain of the problem. The proposed method of this paper has been used to solve the Keepaway game with successful results compared to other solutions recently proposed. Finally, some conclusions are included together with some future lines of work.Facultad de Informática2005-12info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArticulohttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdf211-217http://sedici.unlp.edu.ar/handle/10915/9595enginfo:eu-repo/semantics/altIdentifier/url/http://journal.info.unlp.edu.ar/wp-content/uploads/JCST-Dec05-8.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:UNLP2025-09-29T10:50:44Zoai:sedici.unlp.edu.ar:10915/9595Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 10:50:45.057SEDICI (UNLP) - Universidad Nacional de La Platafalse |
dc.title.none.fl_str_mv |
Cyclic evolution: a new strategy for improving controllers obtained by layered evolution |
title |
Cyclic evolution: a new strategy for improving controllers obtained by layered evolution |
spellingShingle |
Cyclic evolution: a new strategy for improving controllers obtained by layered evolution Olivera, Javier Hugo Ciencias Informáticas Neural nets incremental evolution layered evolution |
title_short |
Cyclic evolution: a new strategy for improving controllers obtained by layered evolution |
title_full |
Cyclic evolution: a new strategy for improving controllers obtained by layered evolution |
title_fullStr |
Cyclic evolution: a new strategy for improving controllers obtained by layered evolution |
title_full_unstemmed |
Cyclic evolution: a new strategy for improving controllers obtained by layered evolution |
title_sort |
Cyclic evolution: a new strategy for improving controllers obtained by layered evolution |
dc.creator.none.fl_str_mv |
Olivera, Javier Hugo Lanzarini, Laura Cristina |
author |
Olivera, Javier Hugo |
author_facet |
Olivera, Javier Hugo Lanzarini, Laura Cristina |
author_role |
author |
author2 |
Lanzarini, Laura Cristina |
author2_role |
author |
dc.subject.none.fl_str_mv |
Ciencias Informáticas Neural nets incremental evolution layered evolution |
topic |
Ciencias Informáticas Neural nets incremental evolution layered evolution |
dc.description.none.fl_txt_mv |
Complex control tasks may be solved by dividing them into a more specific and more easily handled subtasks hierarchy. Several authors have demonstrated that the incremental layered evolution paradigm allows obtaining controllers capable of solving this type of tasks. In this direction, different solutions combining Incremental Evolution with Evolving Neural Networks have been developed in order to provide an adaptive mechanism minimizing the previous knowledge necessary to obtain a good performance giving place to controllers made up of several networks. This paper is focused on the presentation of a new mechanism, called Cyclic Evolution, which allows improving controllers based on neural networks obtained through layered evolution. Its performance is based on continuing the cyclic improvement of each of the networks making up the controller within the whole domain of the problem. The proposed method of this paper has been used to solve the Keepaway game with successful results compared to other solutions recently proposed. Finally, some conclusions are included together with some future lines of work. Facultad de Informática |
description |
Complex control tasks may be solved by dividing them into a more specific and more easily handled subtasks hierarchy. Several authors have demonstrated that the incremental layered evolution paradigm allows obtaining controllers capable of solving this type of tasks. In this direction, different solutions combining Incremental Evolution with Evolving Neural Networks have been developed in order to provide an adaptive mechanism minimizing the previous knowledge necessary to obtain a good performance giving place to controllers made up of several networks. This paper is focused on the presentation of a new mechanism, called Cyclic Evolution, which allows improving controllers based on neural networks obtained through layered evolution. Its performance is based on continuing the cyclic improvement of each of the networks making up the controller within the whole domain of the problem. The proposed method of this paper has been used to solve the Keepaway game with successful results compared to other solutions recently proposed. Finally, some conclusions are included together with some future lines of work. |
publishDate |
2005 |
dc.date.none.fl_str_mv |
2005-12 |
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/10915/9595 |
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http://sedici.unlp.edu.ar/handle/10915/9595 |
dc.language.none.fl_str_mv |
eng |
language |
eng |
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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 211-217 |
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