Inproceedings1821: Unterschied zwischen den Versionen
K (Added from ontology) |
K (Added from ontology) |
||
Zeile 28: | Zeile 28: | ||
|Link=http://dx.doi.org/10.1007/978-3-540-89694-4_12 | |Link=http://dx.doi.org/10.1007/978-3-540-89694-4_12 | ||
|DOI Name= | |DOI Name= | ||
− | |||
|Projekt=OCCS, | |Projekt=OCCS, | ||
|Forschungsgruppe= | |Forschungsgruppe= | ||
+ | }} | ||
+ | {{Forschungsgebiet Auswahl | ||
+ | |Forschungsgebiet=Organic Computing | ||
}} | }} |
Version vom 15. August 2009, 11:33 Uhr
Improving XCS Performance by Distribution
Improving XCS Performance by Distribution
Published: 2008
Buchtitel: Proceedings of the 7th International Conference on Simulated Evolution And Learning (SEAL 2008)
Ausgabe: 5361
Reihe: LNCS
Seiten: 111-120
Verlag: Springer
Referierte Veröffentlichung
BibTeX
Kurzfassung
Learning Classifier Systems (LCS) are rule-based evolutionary reinforcement learning (RL) systems. Today, especially variants of Wilson's eXtended Classifier System (XCS) are widely applied for machine learning. Despite their widespread application, LCSs have drawbacks: The number of reinforcement cycles an LCS requires for learning largely depends on the complexity of the learning task. A straightforward way to reduce this complexity is to split the task into smaller sub-problems. Whenever this can be done, the performance should be improved significantly. In this paper, a nature-inspired multi-agent scenario is used to evaluate and compare different distributed LCS variants. Results show that improvements in learning speed can be achieved by cleverly dividing a problem into smaller learning sub-problems.
ISBN: 978-3-540-89693-7
ISSN: 0302-9743
Weitere Informationen unter: Link
Organic Computing, Maschinelles Lernen