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Application of Symbolic Regression on Blast Furnace and Temper Mill Datasets

  • Michael Kommenda
  • Gabriel Kronberger
  • Christoph Feilmayr
  • Leonhard Schickmair
  • Michael Affenzeller
  • Stephan M. Winkler
  • Stefan Wagner
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6927)

Abstract

This work concentrates on three different modifications of a genetic programming system for symbolic regression analysis. The coefficient of correlation R 2 is used as fitness function instead of the mean squared error and offspring selection is used to ensure a steady improvement of the achieved solutions. Additionally, as the fitness evaluation consumes most of the execution time, the generated solutions are only evaluated on parts of the training data to speed up the whole algorithm. These three algorithmic adaptations are incorporated in the symbolic regression algorithm and their impact is tested on two real world datasets describing a blast furnace and a temper mill process. The effect on the achieved solution quality as well as on the produced models are compared to results generated by a symbolic regression algorithm without the mentioned modifications and the benefits are highlighted.

Keywords

Symbolic Regression Genetic Programming Offspring Selection 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Michael Kommenda
    • 1
  • Gabriel Kronberger
    • 1
  • Christoph Feilmayr
    • 2
  • Leonhard Schickmair
    • 2
  • Michael Affenzeller
    • 1
  • Stephan M. Winkler
    • 1
  • Stefan Wagner
    • 1
  1. 1.Heuristic and Evolutionary Algorithms Laboratory School of Informatics, Communications and MediaUpper Austria University of Applied SciencesHagenbergAustria
  2. 2.voestalpine Stahl GmbHLinzAustria

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