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Learning Potential Inheritance in Baldwinian Evolution
https://doi.org/10.15083/00004055
https://doi.org/10.15083/0000405549af7f8f-e576-4660-9ec8-52bffc0ff901
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
37097408.pdf (1.9 MB)
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| Item type | 学位論文 / Thesis or Dissertation(1) | |||||
|---|---|---|---|---|---|---|
| 公開日 | 2012-10-29 | |||||
| タイトル | ||||||
| タイトル | Learning Potential Inheritance in Baldwinian Evolution | |||||
| 言語 | ||||||
| 言語 | eng | |||||
| 資源タイプ | ||||||
| 資源 | http://purl.org/coar/resource_type/c_46ec | |||||
| タイプ | thesis | |||||
| ID登録 | ||||||
| ID登録 | 10.15083/00004055 | |||||
| ID登録タイプ | JaLC | |||||
| その他のタイトル | ||||||
| その他のタイトル | Baldwin進化における学習能力の遺伝 | |||||
| 著者 |
Liu, Shu
× Liu, Shu |
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| 著者別名 | ||||||
| 識別子Scheme | WEKO | |||||
| 識別子 | 9372 | |||||
| 姓名 | リュウ, ジュ | |||||
| 著者所属 | ||||||
| 著者所属 | 東京大学大学院工学系研究科電気系工学専攻 | |||||
| 著者所属 | ||||||
| 著者所属 | Department of Electrical Engineering and Information Systems, Graduate School of Engineering, The University of Tokyo | |||||
| Abstract | ||||||
| 内容記述タイプ | Abstract | |||||
| 内容記述 | Inspired by organism evolution, evolutionary algorithms have attracted research interests for several decades, and have been proved effective and efficient to solve optimization problems. For further enhancement, local search are introduced into evolution, imitating the biological fact that organism individuals change themselves to better fit the environment during their lifetime. In the context of evolutionary computation, Baldwinian evolution hybridizes global search through population-based evolution with local search through individual refinements. Refinement influences selection, however, unlike Lamarckian evolution, refined traits are not inherited by the offspring. In Baldwinian evolution, local search guides evolution in an indirect manner, termed "the Baldwin effect". Conventional studies revealed that Baldwinian learning can enhance search, however, there is still considerable work to be done to understand the mechanisms. This thesis investigates Baldwinian evolution's mechanisms in depth. By proposing the method of analyzing individual dynamics, we present some novel views. We find that the substantial inheritance in Baldwinian evolution is the potential to achieve high fitness through learning, that the realization of learning potential is influenced by inheritable and noninheritable factors in evolution, and that learning cost penalties inhibit learning intensity. Our results provide knowledge of Baldwinian evolution's mechanisms, and directions to possible applications. | |||||
| 書誌情報 | 発行日 2012-09-27 | |||||
| 日本十進分類法 | ||||||
| 主題Scheme | NDC | |||||
| 主題 | 141 | |||||
| 学位名 | ||||||
| 学位名 | 博士(工学) | |||||
| 学位 | ||||||
| 値 | doctoral | |||||
| 学位分野 | ||||||
| Engineering (工学) | ||||||
| 学位授与機関 | ||||||
| 学位授与機関名 | University of Tokyo (東京大学) | |||||
| 研究科・専攻 | ||||||
| Department of Electrical Engineering and Information Systems, Graduate School of Engineering (工学系研究科電気系工学専攻) | ||||||
| 学位授与年月日 | ||||||
| 学位授与年月日 | 2012-09-27 | |||||
