Learning classifier systems: a survey

Olivier Sigaud, Stewart W. Wilson
2007 Soft Computing - A Fusion of Foundations, Methodologies and Applications  
Learning Classifier Systems (LCSs) are rule-based systems that automatically build their ruleset. At the origin of Holland's work, LCSs were seen as a model of the emergence of cognitive abilities thanks to adaptive mechanisms, particularly evolutionary processes. After a renewal of the field more focused on learning, LCSs are now considered as sequential decision problem-solving systems endowed with a generalization property. Indeed, from a Reinforcement Learning point of view, LCSs can be
more » ... as learning systems building a compact representation of their problem thanks to generalization. More recently, LCSs have proved efficient at solving automatic classification tasks. The aim of the present contribution is to describe the state-of-the-art of LCSs, emphasizing recent developments, and focusing more on the sequential decision domain than on automatic classification.
doi:10.1007/s00500-007-0164-0 fatcat:yaa6csslmfavdilydz3zahgqka