Abstract
A classifier system is a machine learning system that learns syntactically simple string rules called classifiers. Such systems combine learning and evolution processes. The Bucket Brigade algorithm implements the first one, while the second one often use a genetic algorithm. Unfortunately, this kind of genetics-based machine learning systems suffers from a lot of problems yielding system instability, often resulting in poor performance. The main difficulty consists in maintaining good classifiers in the population during the evolution process.
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References
L.D. Davis. Handbook of Genetics algorithms. VNR Computer Library, 1991.
K.A. De Jong. An analysis of the behavior of a class of genetic adaptive systems. PhD thesis, University of Michigan, 1975.
D.E. Goldberg. Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley, 1989.
J.H. Holland. Escaping brittleness: The possibilities of general purpose learning algorithms applied to parallel rule-based systems. In R.S. Michalski, J.G. Carbonell, and T.M. Mitchell, editors, Machine Learning II, pages 593–623. Morgan Kaufmann, 1986.
James R. Levenick. Inserting introns improves genetic algorithms success rate: Taking a cue from biology. In R.K. Belew and L.B. Booker, editors, Genetic algorithms and their applications: Proceedings of the Fourth International Conference on Genetic Algorithms, pages 123–127, San Mateo, CA, 1991. Morgan Kaufmann.
R.L. Riolo. Bucket brigade performance: 1. long sequences of classifiers, 2. default hierarchies. In J.J. Grefenstette, editor, Genetic algorithms and their applications: Proceedings of the Second International Conference on Genetic Algorithms, pages 184–201, Hillsdale, NJ, 1987. Lawrence Erlbaum Associates.
L. Shu and J. Schaeffer. Vcs: Variable classifier systems. In J.D. Schaffer, editor, Genetic algorithms and their applications: Proceedings of the Third International Conference on Genetic Algorithms, pages 334–339, San Mateo, CA, 1989. Morgan Kaufmann.
M. Vose. Generalizing the notion of schema in genetic algorithms. Artificial Intelligence, 50: 385–396, 1991.
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© 1995 Springer-Verlag/Wien
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Collard, P., Escazut, C. (1995). DCS: A Promising Classifier System. In: Artificial Neural Nets and Genetic Algorithms. Springer, Vienna. https://doi.org/10.1007/978-3-7091-7535-4_6
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DOI: https://doi.org/10.1007/978-3-7091-7535-4_6
Publisher Name: Springer, Vienna
Print ISBN: 978-3-211-82692-8
Online ISBN: 978-3-7091-7535-4
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