Inproceedings1821
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
Weitere Informationen unter: Link
DOI Link: 10.1007/978-3-540-89694-4_12
Organic Computing, Maschinelles Lernen