Article

Binary rule encoding schemes: a study using the compact classifier system

Authors:

Xavier Llorà,

Kumara Sastry,

David E. GoldbergAuthors Info & Claims

GECCO '05: Proceedings of the 7th annual workshop on Genetic and evolutionary computation

Pages 88 - 89

https://doi.org/10.1145/1102256.1102275

Published: 25 June 2005 Publication History

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Abstract

Several binary rule encoding schemes have been proposed for Pittsburgh-style classifier systems. This paper focus on the analysis of how rule encoding may bias the scalability of learning maximally general and accurate rules by classifier systems. The theoretical analysis of maximally general and accurate rules using two different binary rule encoding schemes showed some theoretical results with clear implications to the scalability of any genetic-based machine learning system that uses the studied encoding schemes. Such results are clearly relevant since one of the binary representations studied is widely used on Pittsburgh-style classifier systems, and shows an exponential shrink of the useful rules available as the problem size increases.

References

[1]

M. Butz, M. Pelikan, X. Llorà, and David E. Goldberg. Automated Global Structure Extraction For Effective Local Building Block Processing in XCS. IlliGAL Report No. 2005011, University of Illinois at Urbana-Champaign, Illinois Genetic Algorithms Laboratory, Urbana, IL, 2005.

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Kenneth A. De Jong and William M. Spears. Learning Concept Classification Rules using Genetic Algorithms. In Proceedings of the Twelfth International Conference on Artificial Intelligence IJCAI-91, volume 2, pages 651--656. Morgan Kaufmann, 1991.

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D. E. Goldberg. Computer-aided gas pipeline operation using genetic algorithms and rule learning. Dissertation Abstracts International, 44(10):3174B, 1983. Doctoral dissertation, University of Michigan.

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J. H. Holland. Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor, MI, 1975.

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[5]

X. Llorà, Kumara Sastry, and David E. Goldberg. The Compact Classifier System: Motivation, Analysis, and First Results. In Proceedings of the Genetic and Evolutinary Computation Conference (GECCO 2005), page in press. ACM press, 2005.

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Stewart W. Wilson. Classifier fitness based on accuracy. Evolutionary Computation, 3(2):149--175, 1995.

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Cited By

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Kovacs T(2012)Genetics-Based Machine LearningHandbook of Natural Computing10.1007/978-3-540-92910-9_30(937-986)Online publication date: 2012
https://doi.org/10.1007/978-3-540-92910-9_30
Rojanavasu PDam HAbbass HLokan CPinngern O(2009)A self-organized, distributed, and adaptive rule-based induction systemIEEE Transactions on Neural Networks10.1109/TNN.2008.200833420:3(446-459)Online publication date: 1-Mar-2009
https://dl.acm.org/doi/10.1109/TNN.2008.2008334
Lanzi P(2008)Learning classifier systems: then and nowEvolutionary Intelligence10.1007/s12065-007-0003-31:1(63-82)Online publication date: 8-Feb-2008
https://doi.org/10.1007/s12065-007-0003-3
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Index Terms

Binary rule encoding schemes: a study using the compact classifier system
1. Computing methodologies
  1. Machine learning

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Published In

GECCO '05: Proceedings of the 7th annual workshop on Genetic and evolutionary computation

June 2005

431 pages

ISBN:9781450378000

DOI:10.1145/1102256

Conference Chair:
Franz Rothlauf
University of Mannheim, Germany

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 June 2005

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GECCO05

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SIGEVO

GECCO05: Genetic and Evolutionary Computation Conference

June 25 - 26, 2005

D.C., Washington

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

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Kovacs T(2012)Genetics-Based Machine LearningHandbook of Natural Computing10.1007/978-3-540-92910-9_30(937-986)Online publication date: 2012
https://doi.org/10.1007/978-3-540-92910-9_30
Rojanavasu PDam HAbbass HLokan CPinngern O(2009)A self-organized, distributed, and adaptive rule-based induction systemIEEE Transactions on Neural Networks10.1109/TNN.2008.200833420:3(446-459)Online publication date: 1-Mar-2009
https://dl.acm.org/doi/10.1109/TNN.2008.2008334
Lanzi P(2008)Learning classifier systems: then and nowEvolutionary Intelligence10.1007/s12065-007-0003-31:1(63-82)Online publication date: 8-Feb-2008
https://doi.org/10.1007/s12065-007-0003-3
Llorà XSastry KGoldberg DO'Reilly U(2005)The compact classifier systemProceedings of the 7th annual conference on Genetic and evolutionary computation10.1145/1068009.1068328(1993-1994)Online publication date: 25-Jun-2005
https://dl.acm.org/doi/10.1145/1068009.1068328

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