Learning classifier systems: a survey

Authors:
Olivier Sigaud;Stewart W. Wilson
Affiliations:
Université Pierre et Marie Curie - Paris 6, 4 Place Jussieu, 75252, Paris Cedex 05, France;Prediction Dynamics, 4 Place Jussieu, 01742, Concord, MA, USA
Venue:
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Year:
2007

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Abstract

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 seen 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.