Learning classifier system

From Wikipedia, the free encyclopedia
Jump to: navigation, search

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.[citation needed] Despite this, they have been successfully applied in many problem domains.

References[edit]

  • Urbanowicz, Ryan J.; Moore, Jason H. (January 2009), Learning Classifier Systems: A Complete Introduction, Review, and Roadmap, J. Artif. Evol. App. (New York, NY, United States: Hindawi Publishing Corp.) 2009: 1:1–1:25, doi:10.1155/2009/736398 .

External links[edit]