Problem Driven Machine Learning by Co-evolving Genetic Programming Trees and Rules in a Learning Classifier System-Reference-Cited by-同舟云学术

Problem Driven Machine Learning by Co-evolving Genetic Programming Trees and Rules in a Learning Classifier System

Published:2018 Issue: Volume: Page:55-71
ISSN:1932-0167
Container-title:Genetic Programming Theory and Practice XV
language:
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Author:

Urbanowicz Ryan J.,Yang Ben,Moore Jason H.

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-319-90512-9_4

Reference31 articles.

1. M. Ahluwalia and L. Bull. A genetic programming-based classifier system. In Proceedings of the genetic and evolutionary computation conference, volume 1, pages 11–18, 1999.

2. J. Bacardit, E. K. Burke, and N. Krasnogor. Improving the scalability of rule-based evolutionary learning. Memetic Computing, 1(1):55–67, 2009.

3. E. Bernadó-Mansilla and J. M. Garrell-Guiu. Accuracy-based learning classifier systems: models, analysis and applications to classification tasks. Evolutionary Computation, 11(3):209–238, 2003.

4. A. Bonarini. An introduction to learning fuzzy classifier systems. In Learning Classifier Systems, pages 83–104. Springer, 2000.

5. M. V. Butz and O. Herbort. Context-dependent predictions and cognitive arm control with xcsf. In Proceedings of the 10th annual conference on Genetic and evolutionary computation, pages 1357–1364. ACM, 2008.

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