| Item type |
SIG Technical Reports(1) |
| 公開日 |
2019-03-01 |
| タイトル |
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タイトル |
Aggregated CMA-ES: An Effective and Stable Strategy for Neuron Model Optimization |
| タイトル |
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言語 |
en |
|
タイトル |
Aggregated CMA-ES: An Effective and Stable Strategy for Neuron Model Optimization |
| 言語 |
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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_18gh |
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資源タイプ |
technical report |
| 著者所属 |
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Tokyo Institute of Technology |
| 著者所属 |
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Tokyo Institute of Technology |
| 著者所属 |
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National Institute of Informatics |
| 著者所属(英) |
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en |
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Tokyo Institute of Technology |
| 著者所属(英) |
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en |
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Tokyo Institute of Technology |
| 著者所属(英) |
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en |
|
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National Institute of Informatics |
| 著者名 |
Xu, Han
Takahiro, Shinozaki
Ryota, Kobayashi
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| 著者名(英) |
Xu, Han
Takahiro, Shinozaki
Ryota, Kobayashi
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| 論文抄録 |
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内容記述タイプ |
Other |
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内容記述 |
Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES) is considered as one of the most effective method for black-box optimization issue. In this paper, we apply CMA-ES to the neuron model parameter optimization problem, and compare it with genetic algorithm (GA) and the Nelder-Mead method which are the widely used approaches. To enhanced robustness of CMA-ES, we extend it by making an aggregation of evolution. We analyze a public dataset recorded from a rat neocortical neuron, which shows that the proposed approach achieves higher performance than the conventional methods. |
| 論文抄録(英) |
|
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内容記述タイプ |
Other |
|
内容記述 |
Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES) is considered as one of the most effective method for black-box optimization issue. In this paper, we apply CMA-ES to the neuron model parameter optimization problem, and compare it with genetic algorithm (GA) and the Nelder-Mead method which are the widely used approaches. To enhanced robustness of CMA-ES, we extend it by making an aggregation of evolution. We analyze a public dataset recorded from a rat neocortical neuron, which shows that the proposed approach achieves higher performance than the conventional methods. |
| 書誌レコードID |
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収録物識別子タイプ |
NCID |
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収録物識別子 |
AA12055912 |
| 書誌情報 |
研究報告バイオ情報学（BIO）
巻 2019-BIO-57,
号 9,
p. 1-2,
発行日 2019-03-01
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| ISSN |
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収録物識別子タイプ |
ISSN |
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収録物識別子 |
2188-8590 |
| Notice |
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SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc. |
| 出版者 |
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言語 |
ja |
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出版者 |
情報処理学会 |
IPSJ-BIO19057009.pdf (629.1 kB)
