Fuzzy Logic pp 345-354 | Cite as

Fuzzy Classifier Systems

  • Andreas Geyer-Schulz
Part of the Theory and Decision Library book series (TDLD, volume 12)


Fuzzy classifier systems are genetic based machine learning systems which integrate a fuzzy rule base, a genetic algorithm and an apportionment of credit function. In this paper we present a Monte-Carlo selection rule which enables us to give a global convergence proof for (fuzzy) classifier systems and thus combines the advantages of genetic algorithms and simulated annealing algorithms. With the help of the representation theorem we define a mapping from a fuzzy rule language to a crisp rule language and we compare the complexity of the resulting crisp and fuzzy classifier systems. We prove that in the context of genetic based machine learning the performance of the fuzzy version is better than the crisp version.


Genetic Algorithm Fuzzy Rule Rule Base Fuzzy Control Representation Theorem 
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 Science+Business Media Dordrecht 1993

Authors and Affiliations

  • Andreas Geyer-Schulz
    • 1
  1. 1.Department of Applied Computer ScienceInstitute of Information Processing and Information Economics Vienna University of Economics and Business AdministrationAustria

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