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  1. 113 工学系研究科・工学部
  2. 22 電気系工学専攻
  3. 1132220 博士論文(電気系工学専攻)
  1. 0 資料タイプ別
  2. 20 学位論文
  3. 021 博士論文

Learning Potential Inheritance in Baldwinian Evolution

https://doi.org/10.15083/00004055
https://doi.org/10.15083/00004055
49af7f8f-e576-4660-9ec8-52bffc0ff901
名前 / ファイル ライセンス アクション
37097408.pdf 37097408.pdf (1.9 MB)
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

WEKO 9371

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
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