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Maximizing adaptive power in neuroevolution
In this paper we compare systematically the most promising neuroevolutionary methods and two new original methods on the double-pole balancing problem with respect to: the ability to discover solutions that are robust to variations of the environment, the speed with which such solutions are found, a...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6051599/ https://www.ncbi.nlm.nih.gov/pubmed/30020942 http://dx.doi.org/10.1371/journal.pone.0198788 |
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author | Pagliuca, Paolo Milano, Nicola Nolfi, Stefano |
author_facet | Pagliuca, Paolo Milano, Nicola Nolfi, Stefano |
author_sort | Pagliuca, Paolo |
collection | PubMed |
description | In this paper we compare systematically the most promising neuroevolutionary methods and two new original methods on the double-pole balancing problem with respect to: the ability to discover solutions that are robust to variations of the environment, the speed with which such solutions are found, and the ability to scale-up to more complex versions of the problem. The results indicate that the two original methods introduced in this paper and the Exponential Natural Evolutionary Strategy method largely outperform the other methods with respect to all considered criteria. The results collected in different experimental conditions also reveal the importance of regulating the selective pressure and the importance of exposing evolving agents to variable environmental conditions. The data collected and the results of the comparisons are used to identify the most effective methods and the most promising research directions. |
format | Online Article Text |
id | pubmed-6051599 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-60515992018-07-27 Maximizing adaptive power in neuroevolution Pagliuca, Paolo Milano, Nicola Nolfi, Stefano PLoS One Research Article In this paper we compare systematically the most promising neuroevolutionary methods and two new original methods on the double-pole balancing problem with respect to: the ability to discover solutions that are robust to variations of the environment, the speed with which such solutions are found, and the ability to scale-up to more complex versions of the problem. The results indicate that the two original methods introduced in this paper and the Exponential Natural Evolutionary Strategy method largely outperform the other methods with respect to all considered criteria. The results collected in different experimental conditions also reveal the importance of regulating the selective pressure and the importance of exposing evolving agents to variable environmental conditions. The data collected and the results of the comparisons are used to identify the most effective methods and the most promising research directions. Public Library of Science 2018-07-18 /pmc/articles/PMC6051599/ /pubmed/30020942 http://dx.doi.org/10.1371/journal.pone.0198788 Text en © 2018 Pagliuca et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Pagliuca, Paolo Milano, Nicola Nolfi, Stefano Maximizing adaptive power in neuroevolution |
title | Maximizing adaptive power in neuroevolution |
title_full | Maximizing adaptive power in neuroevolution |
title_fullStr | Maximizing adaptive power in neuroevolution |
title_full_unstemmed | Maximizing adaptive power in neuroevolution |
title_short | Maximizing adaptive power in neuroevolution |
title_sort | maximizing adaptive power in neuroevolution |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6051599/ https://www.ncbi.nlm.nih.gov/pubmed/30020942 http://dx.doi.org/10.1371/journal.pone.0198788 |
work_keys_str_mv | AT pagliucapaolo maximizingadaptivepowerinneuroevolution AT milanonicola maximizingadaptivepowerinneuroevolution AT nolfistefano maximizingadaptivepowerinneuroevolution |