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|Title=Improving XCS Performance by Distribution | |Title=Improving XCS Performance by Distribution | ||
|Year=2008 | |Year=2008 | ||
+ | |Month=Dezember | ||
|Booktitle=Proceedings of the 7th International Conference on Simulated Evolution And Learning (SEAL 2008) | |Booktitle=Proceedings of the 7th International Conference on Simulated Evolution And Learning (SEAL 2008) | ||
|Pages=111-120 | |Pages=111-120 | ||
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|ISBN=978-3-540-89693-7 | |ISBN=978-3-540-89693-7 | ||
|ISSN=0302-9743 | |ISSN=0302-9743 | ||
− | | | + | |DOI Name=10.1007/978-3-540-89694-4_12 |
− | + | |Projekt=OCCS | |
− | | | + | |Forschungsgruppe=Effiziente Algorithmen |
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− | | | + | {{Forschungsgebiet Auswahl |
+ | |Forschungsgebiet=Maschinelles Lernen | ||
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{{Forschungsgebiet Auswahl | {{Forschungsgebiet Auswahl | ||
|Forschungsgebiet=Organic Computing | |Forschungsgebiet=Organic Computing | ||
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Version vom 16. September 2009, 11:57 Uhr
Improving XCS Performance by Distribution
Improving XCS Performance by Distribution
Published: 2008
Dezember
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
DOI Link: 10.1007/978-3-540-89694-4_12
Organic Computing, Maschinelles Lernen