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ID 28458
本文ファイル
Thumnail A1206.pdf 747 KB
著者
Karatsu, Naoya
Nagata, Yuichi
Ono, Isao
Kobayashi, Shigenobu
NDC
技術・工学
抄録(英)
When attempting to optimize a function where exists several big-valley structures, conventional GAs often fail to find the global optimum. Innately Split Model (ISM) is a framework of GAs, which is designed to avoid this phenomenon called UV -Phenomenon. However, ISM doesn't care about previouslysearched areas by the past populations. Thus, it is possible that populations of ISM waste evaluation cost for redundant searches reaching previously-found optima. In this paper, we introduce Extended ISM (EISM) that uses search information of past populations as trap to suppress overlapping searches. To show performance of EISM, we apply it to some test functions, and analyze the behavior.
掲載誌名
5th International Workshop on Computational Intelligence & Applications Proceedings : IWCIA 2009
開始ページ
284
終了ページ
289
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