Learning Classifier Systems: A Complete Introduction, Review, and Roadmap. (25th August 2009)

• Record Type: Journal Article
• Authors: Urbanowicz, Ryan J.; Moore, Jason H.
• Other Names: Ritchie Marylyn Academic Editor.
• Is Part Of: Journal of artificial evolution and applications. Volume 2009(2009)
• Publication Date: 2009-08-25
• Subjects: Evolutionary programming (Computer science) -- Periodicals; Evolutionary programming (Computer science); Periodicals; Electronic journals; 006.3823
• DOI: 10.1155/2009/736398
• Languages: English
• ISSNs: 1687-6229

Abstract

Abstract : 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.