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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= | + | |Month=Dezember |
+ | |Booktitle=Proceedings of the 8th International Conference on Simulated Evolution And Learning (SEAL-2010) | ||
+ | |Pages=289-298 | ||
|Publisher=Springer | |Publisher=Springer | ||
− | | | + | |Address=Berlin Heidelberg |
+ | |Editor=Kalyanmoy Deb and others | ||
+ | |Series=LNCS | ||
+ | |Volume=6457 | ||
}} | }} | ||
{{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 | ||
+ | |DOI Name=10.1007/978-3-642-17298-4_30 | ||
|Projekt=OCCS (Phase III) | |Projekt=OCCS (Phase III) | ||
|Forschungsgruppe=Effiziente Algorithmen | |Forschungsgruppe=Effiziente Algorithmen | ||
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{{Forschungsgebiet Auswahl | {{Forschungsgebiet Auswahl | ||
|Forschungsgebiet=Maschinelles Lernen | |Forschungsgebiet=Maschinelles Lernen | ||
+ | }} | ||
+ | {{Forschungsgebiet Auswahl | ||
+ | |Forschungsgebiet=Organic Computing | ||
}} | }} | ||
{{Forschungsgebiet Auswahl}} | {{Forschungsgebiet Auswahl}} |
Aktuelle Version vom 15. März 2011, 13:22 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
Dezember
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
Erscheinungsort: Berlin Heidelberg
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
DOI Link: 10.1007/978-3-642-17298-4_30
Organic Computing, Maschinelles Lernen