An Evolutionary Multi-Objective Optimization-Based Constructive Method for Learning Classifier Systems Adjusting to Non-Markov Environments
Use this link to cite this item : https://ir.lib.hiroshima-u.ac.jp/00028461
ID | 28461 |
file | |
creator |
Moriwake, Keita
Nishizaki, Ichiro
Hayashida, Tomohiro
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NDC |
Technology. Engineering
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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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journal title |
5th International Workshop on Computational Intelligence & Applications Proceedings : IWCIA 2009
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start page | 132
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end page | 136
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date of issued | 2009-11
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publisher | IEEE SMC Hiroshima Chapter
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issn | 1883-3977
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language |
eng
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nii type |
Conference Paper
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HU type |
Conference Papers
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DCMI type | text
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format | application/pdf
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text version | publisher
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rights | (c) Copyright by IEEE SMC Hiroshima Chapter.
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relation url | |
department |
Graduate School of Engineering
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