An Evolutionary Multi-Objective Optimization-Based Constructive Method for Learning Classifier Systems Adjusting to Non-Markov Environments

5th International Workshop on Computational Intelligence & Applications Proceedings : IWCIA 2009 Page 132-136 published_at 2009-11

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Title ( eng )	An Evolutionary Multi-Objective Optimization-Based Constructive Method for Learning Classifier Systems Adjusting to Non-Markov Environments
Creator	Moriwake Keita Katagiri Hideki Nishizaki Ichiro Hayashida Tomohiro
Source Title	5th International Workshop on Computational Intelligence & Applications Proceedings : IWCIA 2009
Start Page	132
End Page	136
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.
NDC	Technology. Engineering [ 500 ]
Language	eng
Resource Type	conference paper
Publisher	IEEE SMC Hiroshima Chapter
Date of Issued	2009-11
Rights	(c) Copyright by IEEE SMC Hiroshima Chapter.
Publish Type	Version of Record
Access Rights	open access
Source Identifier	[ISSN] 1883-3977 [URI] http://www.hil.hiroshima-u.ac.jp/iwcia/2009/