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


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.

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    The abbreviation SASEGASA stands for Self Adaptive Segregative Genetic Algorithm with aspects of Simulated Annealing.

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Corresponding author

Correspondence to Stephan M. Winkler.

Additional information

The work described in this paper was done within the Translational Research Project L284-N04 “GP-Based Techniques for the Design of Virtual Sensors” sponsored by the Austrian Science Fund (FWF).

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Winkler, S.M., Affenzeller, M. & Wagner, S. Using enhanced genetic programming techniques for evolving classifiers in the context of medical diagnosis. Genet Program Evolvable Mach 10, 111–140 (2009).

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

JEL Classification

  • C02
  • C61
  • C65
  • C67
  • C80
  • I29