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Improving the Parsimony of Regression Models for an Enhanced Genetic Programming Process

  • Alexandru-Ciprian Zăvoianu
  • Gabriel Kronberger
  • Michael Kommenda
  • Daniela Zaharie
  • Michael Affenzeller
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6927)

Abstract

This research is focused on reducing the average size of the solutions generated by an enhanced GP process without affecting the high predictive accuracy the method exhibits when being applied on a complex, industry proposed, regression problem. As such, the effects the GP enhancements have on bloat have been studied and, finally, a bloat control system based on dynamic depth limiting (DDL) and iterated tournament pruning (ITP) was designed. The resulting bloat control system is able to improve by ≃ 40% the average GP solution parsimony without impacting average solution accuracy.

Keywords

genetic programming symbolic regression solution parsimony bloat control 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Alexandru-Ciprian Zăvoianu
    • 1
  • Gabriel Kronberger
    • 2
  • Michael Kommenda
    • 2
  • Daniela Zaharie
    • 1
  • Michael Affenzeller
    • 2
  1. 1.Department of Computer ScienceWest University of TimişoaraRomania
  2. 2.Heuristic and Evolutionary Algorithms Laboratory (HEAL)Upper Austrian University of Applied SciencesAustria

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