Learning classifier systems: a survey

doi:10.1007/s00500-007-0164-0

Learning classifier systems: a survey

2007-03-29

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DOI：10.1007/s00500-007-0164-0

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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.

Keywords:

learning classifier systems

reinforcement learning

generalization

Learning classifier systems: a survey

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