A Critical Review of Classifier Systems

[Learning] Classifier systems are a kind of rule-based system with general mechanisms for processing rules in parallel, for adaptive generation of new rules, and for testing the effectiveness of existing rules. These mechanisms make possible performance and learning without the " brittleness " characteristic of most expert systems in AI.

[1]  Adaptation , 1926 .

[2]  Arthur L. Samuel,et al.  Some Studies in Machine Learning Using the Game of Checkers , 1967, IBM J. Res. Dev..

[3]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[4]  John H. Holland,et al.  Cognitive systems based on adaptive algorithms , 1977, SGAR.

[5]  John R. Anderson,et al.  Machine learning - an artificial intelligence approach , 1982, Symbolic computation.

[6]  John H. Holland,et al.  Escaping brittleness: the possibilities of general-purpose learning algorithms applied to parallel rule-based systems , 1995 .

[7]  John H. Holland,et al.  Induction: Processes of Inference, Learning, and Discovery , 1987, IEEE Expert.

[8]  Sara J. Graves,et al.  Improving performance of an electrical power expert system with genetic algorithms , 1988, IEA/AIE '88.

[9]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[10]  Ellen R. McGrattan,et al.  Money as a medium of exchange in an economy with artificially intelligent agents , 1990 .

[11]  Sandip Sen,et al.  Newboole: A Fast GBML System , 1990, ML.

[12]  Alexandre Parodi,et al.  An Efficient Classifier System and Its Experimental Comparison with Two Representative Learning Methods on Three Medical Domains , 1991, ICGA.

[13]  Manuel Valenzuela-Rendón,et al.  The Fuzzy Classifier System: A Classifier System for Continuously Varying Variables , 1991, ICGA.

[14]  P. W. Frey,et al.  Letter recognition using Holland-style adaptive classifiers , 2004, Machine Learning.