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|Title=XCS Revisited: A Novel Discovery Component for the eXtended Classifier System | |Title=XCS Revisited: A Novel Discovery Component for the eXtended Classifier System | ||
|Year=2010 | |Year=2010 | ||
− | |Booktitle= | + | |Booktitle=Proceedings of the 8th International Conference on Simulated Evolution And Learning (SEAL-2010) |
|Publisher=Springer | |Publisher=Springer | ||
|Note=Accepted for publication | |Note=Accepted for publication |
Version vom 24. August 2010, 20:42 Uhr
XCS Revisited: A Novel Discovery Component for the eXtended Classifier System
XCS Revisited: A Novel Discovery Component for the eXtended Classifier System
Published: 2010
Buchtitel: Proceedings of the 8th International Conference on Simulated Evolution And Learning (SEAL-2010)
Verlag: Springer
Referierte Veröffentlichung
Note: Accepted for publication
BibTeX
Kurzfassung
The eXtended Classifier System (XCS) is a rule-based evolutionary on-line learning system. Originally proposed by Wilson, XCS combines techniques from reinforcement learning and evolutionary optimization to learn a population of maximally general, but accurate condition-action rules. This paper focuses on the discovery component of XCS that is responsible for the creation and deletion of rules. A novel rule combining mechanism is proposed that infers maximally general rules from the existing population. Rule combining is evaluated for single- and multi-step learning problems using the well-known multiplexer, Woods, and Maze environments. Results indicate that the novel mechanism allows for faster learning rates and a reduced population size compared to the original XCS implementation.
Organic Computing, Maschinelles Lernen