Rule Extraction from Trained Neural Network with Evolutionary Algorithms

  • Urszula Markowska-Kaczmar
  • Marcin Chumieja
Conference paper
Part of the Advances in Soft Computing book series (AINSC, volume 19)


This paper describes a solution to the problem of incomprehensibility of the neural network by introducing simultaneously working Evolutionary Algorithms as a tool for extracting set of rules in the form of if — then. Each Evolutionary Algorithm is working for searching rules describing one class, which is recognized by a Neural Network. The proposed method has been tested on real domains in order to analyze its behavior under various conditions. A comparison with other rule extraction methods is presented as well.


Neural Network Artificial Neural Network Evolutionary Algorithm Crossover Operator Input Pattern 
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

  • Urszula Markowska-Kaczmar
    • 1
  • Marcin Chumieja
    • 1
  1. 1.Wrocław University of TechnologyWrocławPoland

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