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 
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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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

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