ID | 19716 |
Eprint ID | 19716
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FullText URL | |
Author |
Karatsu Naoya
Nagata Yuichi
Ono Isao
Kobayashi Shigenobu
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Abstract | 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 previously-searched 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.
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Published Date | 2009-11-12
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Publication Title |
Proceedings : Fifth International Workshop on Computational Intelligence & Applications
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Volume | volume2009
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Issue | issue1
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Publisher | IEEE SMC Hiroshima Chapter
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Start Page | 284
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End Page | 289
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ISSN | 1883-3977
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NCID | BB00577064
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Content Type |
Conference Paper
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language |
English
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Copyright Holders | IEEE SMC Hiroshima Chapter
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Event Title | 5th International Workshop on Computational Intelligence & Applications IEEE SMC Hiroshima Chapter : IWCIA 2009
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Event Location | 東広島市
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Event Location Alternative | Higashi-Hiroshima City
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File Version | publisher
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Refereed |
True
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Eprints Journal Name | IWCIA
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