An Evolutionary Multiobjective Optimization-Based Learning Classifier System in Non-Markov Environment(<Special Issue>Information Systems and Human Sciences)

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概要

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• 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 multiobjective optimization-based method for constructing LCSs that adjust to non-Markov environments. Our goal is to construct an 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.

• バイオメディカル・ファジィ・システム学会の論文

著者

• Katagiri Hideki

  Graduate School Of Engineering Hiroshima University

• Nishizaki Ichiro

  Graduate School Of Engineering Hiroshima University

• Hayashida Tomohiro

  Graduate School of Engineering Hiroshima University

• Moriwake Keita

  Graduate School of Engineering Hiroshima University

関連論文

• A Possibilistic and Stochastic Programming Approach to Fuzzy Random MST Problems(Neural Networks and Fuzzy Systems, Recent Advances in Circuits and Systems)
• Studies on Stochastic Programming Models Using Possibility and Necessity Measures for Linear Programming Problems with Fuzzy Random Variable Coefficients(Special Section on Papers Selected from ITC-CSCC 2002)
• An Evolutionary Multiobjective Optimization-Based Learning Classifier System in Non-Markov Environment(Information Systems and Human Sciences)
• A GO Similarity Measurement Method Based on Information Contents and Semantic Values(Information Systems and Human Sciences)

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