Journal of Artificial Evolution and Applications
Volume 2009 (2009), Article ID 736398, 25 pages
Review Article

Learning Classifier Systems: A Complete Introduction, Review, and Roadmap

Department of Genetics, Dartmouth College, Hanover, NH 03755, USA

Received 24 November 2008; Accepted 23 June 2009

Academic Editor: Marylyn Ritchie

Copyright © 2009 Ryan J. Urbanowicz and Jason H. Moore. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


If complexity is your problem, learning classifier systems (LCSs) may offer a solution. These rule-based, multifaceted, machine learning algorithms originated and have evolved in the cradle of evolutionary biology and artificial intelligence. The LCS concept has inspired a multitude of implementations adapted to manage the different problem domains to which it has been applied (e.g., autonomous robotics, classification, knowledge discovery, and modeling). One field that is taking increasing notice of LCS is epidemiology, where there is a growing demand for powerful tools to facilitate etiological discovery. Unfortunately, implementation optimization is nontrivial, and a cohesive encapsulation of implementation alternatives seems to be lacking. This paper aims to provide an accessible foundation for researchers of different backgrounds interested in selecting or developing their own LCS. Included is a simple yet thorough introduction, a historical review, and a roadmap of algorithmic components, emphasizing differences in alternative LCS implementations.