ID | 19655 |
Eprint ID | 19655
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FullText URL | |
Author |
Moriwake Keita
Katagiri Hideki
Nishizaki Ichiro
Hayashida Tomohiro
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Abstract | 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.
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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 | 132
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End Page | 136
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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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