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ID 28461
本文ファイル
Thumnail A1209.pdf 827 KB
著者
Moriwake, Keita
Nishizaki, Ichiro
Hayashida, Tomohiro
NDC
技術・工学
抄録(英)
Learning Classifier Systems (LCSs) are rule-based systems that automatically build their rule set so as to get optimal policies through evolutionary processes. This paper considers an evolutionary multi-objective optimization-based constructive method for LCSs that adjust to non-Markov environments. Our goal is to construct a XCSMH (eXtended Classifier System - Memory Hierarchic) that can obtain not only optimal policies but also highly generalized rule sets. Results of numerical experiments show that the proposed method is superior to an existing method with respect to the generality of the obtained rule sets.
掲載誌名
5th International Workshop on Computational Intelligence & Applications Proceedings : IWCIA 2009
開始ページ
132
終了ページ
136
出版年月日
2009-11
出版者
IEEE SMC Hiroshima Chapter
ISSN
1883-3977
言語
英語
NII資源タイプ
会議発表論文
広大資料タイプ
会議発表論文
DCMIタイプ
text
フォーマット
application/pdf
著者版フラグ
publisher
権利情報
(c) Copyright by IEEE SMC Hiroshima Chapter.
関連情報URL
部局名
工学研究科