Learning classifier systems: a complete introduction, review, and roadmap

By Ryan J. Urbanowicz, Jason H. Moore. 2009

312 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/06a3a7ded1cf4915dc69f9f7d2ae30b7b135fc99

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In Artificial Intelligence

In Molecular Biology

Citations in the corpus

Predicting the difficulty of pure, strict, epistatic models: metrics for simulated model selection (2012, methodology): “This novel detection estimation strategy, unique toM-LCS algorithms, is detailed further in [22].”