Current Direction on Evolutionary Rule-learning
Author:
Publisher
Institute of Electrical Engineers of Japan (IEE Japan)
Subject
Electrical and Electronic Engineering
Link
https://www.jstage.jst.go.jp/article/ieejjournal/140/12/140_786/_pdf
Reference18 articles.
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2. (4) M. Nakata, W. Browne, T. Hamagami, and K Takadama:“Theoretical XCS parameter settings of learning accurate classifiers”, Proc. of the Genetic and Evolutionary Computation Conference 2017, pp.473-480 (2017)
3. (5) M. Nakata and W. N. Browne:“Learning Optimality Theory for Accuracy-based Learning Classifier System”, IEEE Transactions on Evolutionary Computation, Early access, IEEE (2020)
4. (6) M. Horiuchi, and M. Nakata:“Self-adaptation of XCS learning parameters based on Learning theory”, Proceedings of the Genetic and Evolutionary Computation Conference 2020, pp.342-349, ACM (2020)
5. (7) M. Nakata, W. Browne, and T. Hamagami:“Theoretical adaptation of multiple rule-generation in XCS”, Proceedings of the Genetic and Evolutionary Computation Conference 2018, pp.482-489, ACM (2018)
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