WEKO3
アイテム
Acceleration of Genetic Programming by Hierarchical Structure Learning: A Case Study on Image Recognition Program Synthesis
http://hdl.handle.net/2237/15003
http://hdl.handle.net/2237/150031272caca-4999-422a-ae93-722c14d80c9e
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
479.pdf (811.9 kB)
|
|
| アイテムタイプ | 学術雑誌論文 / Journal Article(1) | |||||
|---|---|---|---|---|---|---|
| 公開日 | 2011-07-01 | |||||
| タイトル | ||||||
| タイトル | Acceleration of Genetic Programming by Hierarchical Structure Learning: A Case Study on Image Recognition Program Synthesis | |||||
| 言語 | en | |||||
| 著者 |
WATCHAREERUETAI, Ukrit
× WATCHAREERUETAI, Ukrit× MATSUMOTO, Tetsuya× OHNISHI, Noboru× KUDO, Hiroaki× TAKEUCHI, Yoshinori |
|||||
| アクセス権 | ||||||
| アクセス権 | open access | |||||
| アクセス権URI | http://purl.org/coar/access_right/c_abf2 | |||||
| 権利 | ||||||
| 権利情報 | Copyright (C) 2009 IEICE | |||||
| 言語 | en | |||||
| キーワード | ||||||
| 主題Scheme | Other | |||||
| 主題 | hierarchical structure genetic programming | |||||
| キーワード | ||||||
| 主題Scheme | Other | |||||
| 主題 | acceleration | |||||
| キーワード | ||||||
| 主題Scheme | Other | |||||
| 主題 | learning node | |||||
| キーワード | ||||||
| 主題Scheme | Other | |||||
| 主題 | training subsets | |||||
| キーワード | ||||||
| 主題Scheme | Other | |||||
| 主題 | population integration | |||||
| 抄録 | ||||||
| 内容記述タイプ | Abstract | |||||
| 内容記述 | We propose a learning strategy for acceleration in learning speed of genetic programming (GP), named hierarchical structure GP (HSGP). The HSGP exploits multiple learning nodes (LNs) which are connected in a hierarchical structure, e.g., a binary tree. Each LN runs conventional evolutionary process to evolve its own population, and sends the evolved population into the connected higher-level LN. The lower-level LN evolves the population with a smaller subset of training data. The higher-level LN then integrates the evolved population from the connected lower-level LNs together, and evolves the integrated population further by using a larger subset of training data. In HSGP, evolutionary processes are sequentially executed from the bottom-level LNs to the top-level LN which evolves with the entire training data. In the experiments, we adopt conventional GPs and the HSGPs to evolve image recognition programs for given training images. The results show that the use of hierarchical structure learning can significantly improve learning speed of GPs. To achieve the same performance, the HSGPs need only 30-40% of the computation cost needed by conventional GPs. | |||||
| 言語 | en | |||||
| 出版者 | ||||||
| 出版者 | Institute of Electronics, Information and Communication Engineers | |||||
| 言語 | en | |||||
| 言語 | ||||||
| 言語 | eng | |||||
| 資源タイプ | ||||||
| 資源タイプresource | http://purl.org/coar/resource_type/c_6501 | |||||
| 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 | |||||
| 関連情報 | ||||||
| 関連タイプ | isVersionOf | |||||
| 識別子タイプ | URI | |||||
| 関連識別子 | http://www.ieice.org/jpn/trans_online/index.html | |||||
| ISSN | ||||||
| 収録物識別子タイプ | PISSN | |||||
| 収録物識別子 | 0916-8532 | |||||
| 書誌情報 |
en : IEICE transactions on information and systems 巻 E92-D, 号 10, p. 2094-2102, 発行日 2009-10-01 |
|||||
| 著者版フラグ | ||||||
| 値 | publisher | |||||
| URI | ||||||
| 識別子 | http://www.ieice.org/jpn/trans_online/index.html | |||||
| 識別子タイプ | URI | |||||
| URI | ||||||
| 識別子 | http://hdl.handle.net/2237/15003 | |||||
| 識別子タイプ | HDL | |||||
