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ID 19655
Eprint ID
19655
FullText URL
Author
Moriwake Keita
Katagiri Hideki
Nishizaki Ichiro
Hayashida Tomohiro
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.
Published Date
2009-11-12
Publication Title
Proceedings : Fifth International Workshop on Computational Intelligence & Applications
Volume
volume2009
Issue
issue1
Publisher
IEEE SMC Hiroshima Chapter
Start Page
132
End Page
136
ISSN
1883-3977
NCID
BB00577064
Content Type
Conference Paper
language
英語
Copyright Holders
IEEE SMC Hiroshima Chapter
Event Title
5th International Workshop on Computational Intelligence & Applications IEEE SMC Hiroshima Chapter : IWCIA 2009
Event Location
東広島市
Event Location Alternative
Higashi-Hiroshima City
File Version
publisher
Refereed
True
Eprints Journal Name
IWCIA