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A population-based approach to finding the matchset of a learning classifier system efficiently

Published: 08 July 2009 Publication History

Abstract

Profiling of the learning classifier system XCS [11] has revealed that its execution time tends to be dominated by rule matching [8], it is therefore important for rule matching to be efficient. To date, the fastest speedups for matching have been achieved by exploiting parallelism [8], but efficient sequential approaches, such as bitset and "specificity" matching [2], can be utilised if there is no platform support for the vector instruction sets that [8] employs. Previous sequential approaches have focussed on improving the efficiency of matching individual rules; in this paper, we introduce a population-based approach that partially matches many rules simultaneously. This is achieved by maintaining the rule-base in a rooted 3-ary tree over which a backtracking depth-first search is run to find the matchset. We found that the method generally outperformed standard and specificity matching on raw matching and on several benchmarking tasks. While the bitset approach attained the best speedups on the benchmarking tasks, we give an analysis that shows that it can be the least efficient of the approaches on long rule conditions. A limitation of the new method is that it is inefficient when the proportion of "don't care" symbols in the rule conditions is very large, which could perhaps be remedied by combining the method with the specificity technique.

References

[1]
M. V. Butz. XCS (+ tournament selection) classifier system implementation in C, version 1.2. Technical Report 2003023, Illinois Genetic Algorithms Laboratory, University of Illinois at Urbana-Champaign, USA, 2003.
[2]
M. V. Butz, P. L. Lanzi, X. Llora, and D. Loiacono. An analysis of matching in learning classifier systems. In C. Ryan and M. Keijzer, editors, Genetic and Evolutionary Computation Conference, GECCO 2008, pages 1349--1356. ACM Press, 2008.
[3]
M. V. Butz and S. W. Wilson. An algorithmic description of XCS. Soft Computing, 6(3-4):144--153, 2002.
[4]
M. Dorigo and M. Colombetti. Robot Shaping: An Experiment in Behavior Engineering. MIT Press, 1997.
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C. L. Forgy. Rete: A fast algorithm for the many pattern/many object pattern match problem. Artificial Intelligence, 19(1):17--37, 1982.
[6]
T. Kovacs and M. Kerber. What makes a problem hard for XCS? In P. L. Lanzi, W. Stolzman, and S. W. Wilson, editors, Advances in Learning Classifier Systems, volume 1996 of LNCS, pages 80--99. Springer-Verlag, 2001.
[7]
P. L. Lanzi and D. Loiacono. XCSLib: The XCS classifier system library. Technical Report 2009005, Illinois Genetic Algorithms Laboratory, University of Illinois at Urbana-Champaign, IL 61801, USA, 2009.
[8]
X. Llora and K. Sastry. Fast rule matching for learning classifier systems via vector instructions. In M. Cattolico, editor, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2006, pages 1513--1520. ACM Press, 2006.
[9]
C. Stone and L. Bull. For real! XCS with continuous-valued inputs. Evolutionary Computation, 11(3):299--336, 2003.
[10]
S. W. Wilson. ZCS: A zeroth level classifier system. Evolutionary Computation, 2(1):1--18, 1994.
[11]
S. W. Wilson. Classifier fitness based on accuracy. Evolutionary Computation, 3(2):149--175, 1995.
[12]
S. W. Wilson. Generalization in the XCS classifier system. In J. R. Koza, W. Banzhaf, K. Chellapilla, K. Deb, M. Dorigo, D. B. Fogel, M. H. Garzon, D. E. Goldberg, H. Iba, and R. Riolo, editors, Genetic Programming 1998: Proceedings of the Third Annual Conference, pages 665--674, University of Wisconsin, Madison, Wisconsin, USA, 1998. Morgan Kaufmann.
[13]
S. W. Wilson. Get real! XCS with continuous-valued inputs. In P. L. Lanzi, W. Stolzmann, and S. W. Wilson, editors, Learning Classifier Systems: from Foundations to Applications, volume 1813 of LNCS, pages 209--222. Springer-Verlag, 2000.
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S. W. Wilson. Classifiers that approximate functions. Natural Computing, 1(2-3):211--234, 2002.

Cited By

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  • (2013)Large-scale data mining using genetics-based machine learningWiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery10.1002/widm.10783:1(37-61)Online publication date: 1-Jan-2013
  • (2010)Speeding up the evaluation of evolutionary learning systems using GPGPUsProceedings of the 12th annual conference on Genetic and evolutionary computation10.1145/1830483.1830672(1039-1046)Online publication date: 7-Jul-2010

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cover image ACM Conferences
GECCO '09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation
July 2009
2036 pages
ISBN:9781605583259
DOI:10.1145/1569901
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Published: 08 July 2009

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Author Tags

  1. efficient matching
  2. lcs
  3. learning classifier systems
  4. xcs

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GECCO09
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GECCO09: Genetic and Evolutionary Computation Conference
July 8 - 12, 2009
Québec, Montreal, Canada

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View all
  • (2013)Large-scale data mining using genetics-based machine learningWiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery10.1002/widm.10783:1(37-61)Online publication date: 1-Jan-2013
  • (2010)Speeding up the evaluation of evolutionary learning systems using GPGPUsProceedings of the 12th annual conference on Genetic and evolutionary computation10.1145/1830483.1830672(1039-1046)Online publication date: 7-Jul-2010

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