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A Study on the Effect of Cooperative Evolution on Concept Learning

  • Filippo Neri
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2037)

Abstract

A preliminary investigation of the results produced by two cooperative learning strategies exploited in the system REGAL is reported. The objective is to produce a more efficient learning system. An extensive description about how to setup a suitable experimental setup is included. It is worthwhile to note that, in principle, these cooperative learning strategies could be applied to a pool of different learning systems.

Keywords

Genetic Algorithm Learning System Cooperative Strategy Target Concept Hypothesis Space 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    T.G. Dietterich and R.S. Michalski. A comparative review of selected methods for learning from examples. In J.G. Carbonell, R.S. Michalski, and T. Mitchell, editors, Machine Learning, an Artificial Intelligence Approach. Morgan Kaufmann, 1983.Google Scholar
  2. 2.
    R.S. King, S. Muggleton, R.A. Lewis, and M.J.E. Sternberg. Theories for mutagenecity: a study in first order and feature based induction. Artificial Intelligence, 74, 1995.Google Scholar
  3. 3.
    W. Lee, S. Stolfo, and K.W. Mok. Mining audit data to build intrusion detection models. In Knowledge discovery in databases 1998, pages 66–72, Fairfax, VA, 1998.Google Scholar
  4. 4.
    F. Neri. Comparing local search with respect to genetic evolution to detect intrusions in computer networks. In Congress on Evolutionary Computation 2000, pages 512–517, IEEE Press, 2000.Google Scholar
  5. 5.
    J.H. Holland. Adaptation in Natural and Artificial Systems. The University of Michigan Press, Ann Arbor, Mi, 1975.Google Scholar
  6. 6.
    D. Goldberg. Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading, Ma, 1989.zbMATHGoogle Scholar
  7. 7.
    K.A. De Jong, W.M. Spears, and F.D. Gordon. Using genetic algorithms for concept learning. Machine Learning, 13:161–188, 1993.Google Scholar
  8. 8.
    C.Z. Janikow. A knowledge intensive genetic algorithm for supervised learning. Machine Learning, 13:198–228, 1993.CrossRefGoogle Scholar
  9. 9.
    A. Giordana and F. Neri. Search-intensive concept induction. Evolutionary Computation, 3(4):375–416, 1995.CrossRefGoogle Scholar
  10. 10.
    J. Hekanaho. Background knowledge in ga-based concept learning. In 13th International Conference on Machine Learning, pages 234–242, Bari, Italy, 1996.Google Scholar
  11. 11.
    P. Husbands and F. Mill. A theoretical investigation of a parallel genetic algorithm. In Fourth International Conference on Genetic Algorithms, pages 264–270, Fairfax, VA, 1991. Morgan Kaufmann.Google Scholar
  12. 12.
    M. Potter. The Design and Analysis of a Computational Model of Cooperative Co-evolution. PhD thesis, Department of Computer Science. George Mason University, VA, 1997.Google Scholar
  13. 13.
    F. Neri. First Order Logic Concept Learning by means of a Distributed Genetic Algorithm. PhD thesis, Department of Computer Science. University of Torino, Italy, 1997.Google Scholar
  14. 14.
    J.L. Shapiro. Does data-mod co-evolution improve generalization performances of evolving learners? Lecture Notes in Computer Science, LNCS 1498:540–549, 1998.Google Scholar
  15. 15.
    R.E. Schapire. A brief introduction to boosting. pages 1401–1406, 1999.Google Scholar
  16. 16.
    T. Dietterich. An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization. Machine Learning, 40:139–158, 2000.CrossRefGoogle Scholar
  17. 17.
    R. Michalski, I. Mozetic, J. Hong, and N. Lavrac. The multi-purpose incremental learning system AQ15 and its testing application to three medical domains. In Fifth National Conference on Artificial Intelligence, pages 1041–1045, Philadelphia, PA, 1986.Google Scholar
  18. 18.
    J.R. Quinlan. C4.5: Programs for Machine Learning. Morgan Kaufmann, California, 1993.Google Scholar
  19. 19.
    F. Neri and L. Saitta. Exploring the power of genetic search in learning symbolic classifiers. IEEE Trans. on Pattern Analysis and Machine Intelligence, PAMI-18:1135–1142, 1996.CrossRefGoogle Scholar
  20. 20.
    J.S. Schlimmer. Concept acquisition through representational adjustement. Technical Report TR 87-19, Dept. of Information and Computer Science, University of Californina, Irvine, CA, 1987.Google Scholar
  21. 21.
    R. Quinlan. Oversearching and layered search in empirical learning. In International Conference on Machine Learning, Lake Tahoe, CA, 1995.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Filippo Neri
    • 1
  1. 1.Marie Curie Fellow at Unilever ResearchUniversity of Piemonte OrientalePort SunlightItaly

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