Genetic Programming and Evolvable Machines

, Volume 10, Issue 2, pp 111–140 | Cite as

Using enhanced genetic programming techniques for evolving classifiers in the context of medical diagnosis

  • Stephan M. WinklerEmail author
  • Michael Affenzeller
  • Stefan Wagner
Original Paper


There are several data based methods in the field of artificial intelligence which are nowadays frequently used for analyzing classification problems in the context of medical applications. As we show in this paper, the application of enhanced evolutionary computation techniques to classification problems has the potential to evolve classifiers of even higher quality than those trained by standard machine learning methods. On the basis of five medical benchmark classification problems taken from the UCI repository as well as the Melanoma data set (prepared by members of the Department of Dermatology of the Medical University Vienna) we document that the enhanced genetic programming approach presented here is able to produce comparable or even better results than linear modeling methods, artificial neural networks, kNN classification, support vector machines and also various genetic programming approaches.


Adaptation/self-adaptation Data mining Classifier systems Genetic programming Empirical study Medicine 

JEL Classification

C02 C61 C65 C67 C80 I29 


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Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • Stephan M. Winkler
    • 1
    Email author
  • Michael Affenzeller
    • 2
  • Stefan Wagner
    • 2
  1. 1.Research Center Hagenberg, Upper Austria University of Applied SciencesHagenbergAustria
  2. 2.Department of Software EngineeringUpper Austria University of Applied SciencesHagenbergAustria

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