2009 LearningClassifierSystemsACompl

• (Urbanowicz & Moore, 2009) ⇒ Ryan J. Urbanowicz, and Jason H. Moore. (2009). “Learning Classifier Systems: A Complete Introduction, Review, and Roadmap.” In: Journal of Artificial Evolution and Applications, 2009. Hindawi Publishing Corporation. doi:10.1155/2009/736398

Subject Headings: Classifier System.

Cited By

• http://scholar.google.com/scholar?q=%222009%22+Learning+Classifier+Systems%3A+A+Complete+Introduction%2C+Review%2C+and+Roadmap
• http://dl.acm.org/citation.cfm?id=1644490.1644491&preflayout=flat#citedby

Quotes

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.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2009 LearningClassifierSystemsACompl	Ryan J. Urbanowicz Jason H. Moore			Learning Classifier Systems: A Complete Introduction, Review, and Roadmap				10.1155/2009/736398		2009