Evolving Neural Arrays A new mechanism for learning complex action sequences
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 incr...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Centro Latinoamericano de Estudios en Informática
2018-09-01
|
| Series: | CLEI Electronic Journal |
| Subjects: | |
| Online Access: | http://clei.org/cleiej-beta/index.php/cleiej/article/view/348 |
| _version_ | 1818362509903527936 |
|---|---|
| author | Leonardo Corbalán Laura Lanzarini |
| author_facet | Leonardo Corbalán Laura Lanzarini |
| author_sort | Leonardo Corbalán |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-13T21:33:43Z |
| format | Article |
| id | doaj.art-dc44b171c2bf483b9e496ef83ebe5e8a |
| institution | Directory Open Access Journal |
| issn | 0717-5000 |
| language | English |
| last_indexed | 2024-12-13T21:33:43Z |
| publishDate | 2018-09-01 |
| publisher | Centro Latinoamericano de Estudios en Informática |
| record_format | Article |
| series | CLEI Electronic Journal |
| spelling | doaj.art-dc44b171c2bf483b9e496ef83ebe5e8a2022-12-21T23:30:44ZengCentro Latinoamericano de Estudios en InformáticaCLEI Electronic Journal0717-50002018-09-016110.19153/cleiej.6.1.5Evolving Neural Arrays A new mechanism for learning complex action sequencesLeonardo Corbalán0Laura Lanzarini1Universidad Nacional de La Plata, Facultad de InformáticaUniversidad Nacional de La Plata, Facultad de Informática,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.http://clei.org/cleiej-beta/index.php/cleiej/article/view/348Evolving Neural NetsLearningComplex Actions Sequence LearningIncremental EvolutionGenetic Algorithms |
| spellingShingle | Leonardo Corbalán Laura Lanzarini Evolving Neural Arrays A new mechanism for learning complex action sequences CLEI Electronic Journal Evolving Neural Nets Learning Complex Actions Sequence Learning Incremental Evolution Genetic Algorithms |
| title | 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_short | Evolving Neural Arrays A new mechanism for learning complex action sequences |
| title_sort | evolving neural arrays a new mechanism for learning complex action sequences |
| topic | Evolving Neural Nets Learning Complex Actions Sequence Learning Incremental Evolution Genetic Algorithms |
| url | http://clei.org/cleiej-beta/index.php/cleiej/article/view/348 |
| work_keys_str_mv | AT leonardocorbalan evolvingneuralarraysanewmechanismforlearningcomplexactionsequences AT lauralanzarini evolvingneuralarraysanewmechanismforlearningcomplexactionsequences |