| アイテムタイプ |
共通アイテムタイプ(1) |
| 公開日 |
2025-02-28 |
| タイトル |
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|
タイトル |
Improving the performance of mutation-based evolving artificial neural networks with self-adaptive mutations |
|
言語 |
en |
| 著者 |
Hiraga, Motoaki
Komura, Masahiro
Miyamoto, Akiharu
森本, 大智
Ohkura, Kazuhiro
|
| 著作権関連情報 |
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|
権利情報Resource |
http://creativecommons.org/licenses/by/4.0/ |
|
権利情報 |
Copyright (c) 2024 Hiraga et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
|
言語 |
en |
| 抄録 |
|
|
内容記述タイプ |
Abstract |
|
内容記述 |
Neuroevolution is a promising approach for designing artificial neural networks using an evolutionary algorithm. Unlike recent trending methods that rely on gradient-based algorithms, neuroevolution can simultaneously evolve the topology and weights of neural networks. In neuroevolution with topological evolution, handling crossover is challenging because of the competing conventions problem. Mutation-based evolving artificial neural network is an alternative topology and weights neuroevolution approach that omits crossover and uses only mutations for genetic variation. This study enhances the performance of mutation-based evolving artificial neural network in two ways. First, the mutation step size controlling the magnitude of the parameter perturbation is automatically adjusted by a self-adaptive mutation mechanism, enabling a balance between exploration and exploitation during the evolution process. Second, the structural mutation probabilities are automatically adjusted depending on the network size, preventing excessive expansion of the topology. The proposed methods are compared with conventional neuroevolution algorithms using locomotion tasks provided in the OpenAI Gym benchmarks. The results demonstrate that the proposed methods with the self-adaptive mutation mechanism can achieve better performance. In addition, the adjustment of structural mutation probabilities can mitigate topological bloat while maintaining performance. |
|
言語 |
en |
| 書誌情報 |
en : PLoS ONE
巻 19,
号 7,
p. e0307084,
発行日 2024-07-15
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| 出版社 |
|
|
出版者 |
Public Library of Science |
|
言語 |
en |
| 言語 |
|
|
言語 |
eng |
| 資源タイプ |
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|
資源タイプ識別子 |
http://purl.org/coar/resource_type/c_6501 |
|
資源タイプ |
journal article |
| 出版タイプ |
|
|
出版タイプ |
VoR |
|
出版タイプResource |
http://purl.org/coar/version/c_970fb48d4fbd8a85 |
| DOI |
|
|
|
識別子タイプ |
DOI |
|
|
関連識別子 |
https://doi.org/10.1371/journal.pone.0307084 |
| ISSN |
|
|
収録物識別子タイプ |
EISSN |
|
収録物識別子 |
1932-6203 |
| 研究者情報 |
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|
URL |
https://hyokadb02.jimu.kyutech.ac.jp/html/100001769_ja.html |
| 論文ID（連携） |
|
|
値 |
10448925 |
| 連携ID |
|
|
値 |
13064 |
10448925.pdf (2.6 MB)
