A learning classifier system, or LCS, is a machine learning system with close links to reinforcement learning and genetic algorithms. First described by John Holland, his LCS consisted of a population of binary rules on which a genetic algorithm altered and selected the best rules. Rule fitness was based on a reinforcement learning technique.
Learning classifier systems can be split into two types depending upon where the genetic algorithm acts. A Pittsburgh-type LCS has a population of separate rule sets, where the genetic algorithm recombines and reproduces the best of these rule sets. In a Michigan-style LCS there is only a single set of rules in a population and the algorithm's action focuses on selecting the best classifiers within that set. Michigan-style LCSs have two main types of fitness definitions, strength-based (e.g. ZCS) and accuracy-based (e.g. XCS). The term "learning classifier system" most often refers to Michigan-style LCSs.
Initially the classifiers or rules were binary, but recent research has expanded this representation to include real-valued, neural network, and functional (S-expression) conditions.
Learning classifier systems are not fully understood mathematically and doing so remains an area of active research. Despite this, they have been successfully applied in many problem domains.
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