A Comparison of Genetic Programming Variants for Data Classification

  • Jeroen Eggermont
  • Agoston E. Eiben
  • Jano I. van Hemert
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1642)


In this paper we report the results of a comparative study on different variations of genetic programming applied on binary data classiffication problems. The ffirst genetic programming variant is weighting data records for calculating the classiffication error and modifying the weights during the run. Hereby the algorithm is deffining its own ffitness function in an on-line fashion giving higher weights to ‘hard’ records. Another novel feature we study is the atomic representation, where ‘Booleanization’ of data is not performed at the root, but at the leafs of the trees and only Boolean functions are used in the trees’ body. As a third aspect we look at generational and steady-state models in combination of both features.


Genetic Program Boolean Function Constraint Satisfaction Problem Atomic Representation Pima Indian Diabetes 
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 1999

Authors and Affiliations

  • Jeroen Eggermont
    • 1
  • Agoston E. Eiben
    • 1
  • Jano I. van Hemert
    • 1
  1. 1.Leiden UniversityLeidenThe Netherlands

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