Recent advances in Learning Classifier Systems (LCSs) have shown their sequential decision-making ability with a generalization property. In this paper, a novel LCS named eXtended rule-based Genetic Network Programming (XrGNP) is proposed. Different from most of the current LCSs, the rules are represented and discovered through a graph-based evolutionary algorithm GNP, which consequently has the distinct expression ability to model and evolve the decision-making rules. XrGNP is described in details in which its unique features are explicitly mapped. Experiments on benchmark and real-world multi-step problems demonstrate the effectiveness of XrGNP.
|ホスト出版物のタイトル||Proceedings - 2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013|
|出版ステータス||Published - 2013|
|イベント||2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013 - Manchester, United Kingdom|
継続期間: 2013 10 13 → 2013 10 16
|Other||2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013|
|Period||13/10/13 → 13/10/16|
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