Inproceedings3093: Unterschied zwischen den Versionen
Nfr (Diskussion | Beiträge) |
Nfr (Diskussion | Beiträge) |
||
Zeile 24: | Zeile 24: | ||
{{Publikation Details | {{Publikation Details | ||
|Abstract=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. | |Abstract=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. | ||
+ | |ISBN=978-3-642-17297-7 | ||
+ | |Link=http://www.springerlink.com/content/35x2tx0q24366650/ | ||
+ | |DOI Name=10.1007/978-3-642-17298-4_30 | ||
|Projekt=OCCS (Phase III) | |Projekt=OCCS (Phase III) | ||
|Forschungsgruppe=Effiziente Algorithmen | |Forschungsgruppe=Effiziente Algorithmen |
Version vom 3. März 2011, 13:09 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
Herausgeber: Kalyanmoy Deb and others
Buchtitel: Proceedings of the 8th International Conference on Simulated Evolution And Learning (SEAL-2010)
Ausgabe: 6457
Reihe: LNCS
Seiten: 289-298
Verlag: Springer
Referierte Veröffentlichung
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.
ISBN: 978-3-642-17297-7
Weitere Informationen unter: Link
DOI Link: 10.1007/978-3-642-17298-4_30
Organic Computing, Maschinelles Lernen