Learning Fuzzy Classifiers with Evolutionary Algorithms

  • Mauro L. Beretta
  • Andrea G. B. Tettamanzi
Conference paper
Part of the Advances in Soft Computing book series (AINSC, volume 18)


This paper illustrates an evolutionary algorithm, which learns classifiers, represented as sets of fuzzy rules, from a data set containing past experimental observations of a phenomenon. The approach is applied to a benchmark dataset made available by the machine learning community.


Genetic Algorithm Membership Function Evolutionary Algorithm Fuzzy Controller Triangular Membership Function 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    D. W. Aha and D. Kibler. Noise-tolerant instance-based learning algorithms. In Proceedings of the Ilth International Joint Conference on Artificial Intelligence (IJCAI-89), pages 794–799. Morgan Kaufmann, 1989.Google Scholar
  2. 2.
    T. Back. Evolutionary algorithms in theory and practice. Oxford University Press, Oxford, 1996.Google Scholar
  3. 3.
    D. E. Goldberg. Genetic Algorithms in Search, Optimization & Machine Learning. Addison-Wesley, Reading, MA, 1989.MATHGoogle Scholar
  4. 4.
    C. Z. Janikow. A genetic algorithm for learning fuzzy controllers. Proceedings of the ACM Symposium on Applied Computing, New York, 1994. ACM Press.Google Scholar
  5. 5.
    C. L. Karr. Genetic algorithms for fuzzy controllers. AI Expert, March 1991.Google Scholar
  6. 6.
    M. Lee and H. Takagi. Embedding apriori knowledge into an integrated fuzzy system design method based on genetic algorithms. Proceedings of the 5th IFSA World Congress (IFSA’93), pages Vol.\ II, 1293–1296, July 4–9 1993.Google Scholar
  7. 7.
    M. Lee and H. Takagi. Integrating design stages of fuzzy systems using genetic algorithms Proceedings of the 2nd International Conference on Fuzzy Systems (FUZZ-IEEE’93), pages Vol.\ I, 612–617, 1993.Google Scholar
  8. 8.
    Z. Michalewicz. Genetic Algorithms + Data Structures = Evolution Programs. Springer-Verlag, Berlin, 1992.MATHGoogle Scholar
  9. 9.
    P. M. Murphy and D. W. Aha. The UCI repository of machine learning databases and domain theories. URL: http://www.ics.uci.edu/—mlearn/MLRepository.html, December 1995.
  10. 10.
    V. G. Sigillito, S.P. Wing, L. V. Hutton, and K. B. Baker. Classification of radar returns from the ionosphere using neural networks. Johns Hopkins APL Technical Digest, 10: 262–266, 1989.Google Scholar
  11. 11.
    A. Tettamanzi. An evolutionary algorithm for fuzzy controller synthesis and optimization In IEEE International Conference on Systems, Man and Cybernetics, volume 5 /5, pages 4021–4026. IEEE Systems, Man and Cybernetics Society, 1995.Google Scholar
  12. 12.
    P. Thrift. Fuzzy logic synthesis with genetic algorithms. In R. K. Belew and L. B. Booker, editors, Proceedings of the Fourth International Conference on Genetic Algorithms, San Mateo, CA, 1991. Morgan Kaufmann.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Mauro L. Beretta
    • 1
  • Andrea G. B. Tettamanzi
    • 2
  1. 1.Genetica S.r.l.MilanoItaly
  2. 2.Dipartimento di Tecnologie dell’InformazioneUniversità degli Studi di MilanoCrema (CR)Italy

Personalised recommendations