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Knowledge Discovery through Symbolic Regression with HeuristicLab

  • Gabriel Kronberger
  • Stefan Wagner
  • Michael Kommenda
  • Andreas Beham
  • Andreas Scheibenpflug
  • Michael Affenzeller
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7524)

Abstract

This contribution describes how symbolic regression can be used for knowledge discovery with the open-source software HeuristicLab. HeuristicLab includes a large set of algorithms and problems for combinatorial optimization and for regression and classification, including symbolic regression with genetic programming. It provides a rich GUI to analyze and compare algorithms and identified models. This contribution mainly focuses on specific aspects of symbolic regression that are unique to HeuristicLab, in particular, the identification of relevant variables and model simplification.

Keywords

Genetic Programming Knowledge Discovery Heuristic Optimization Symbolic Regression Tower Data 
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.

References

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    Affenzeller, M., Winkler, S., Wagner, S., Beham, A.: Genetic Algorithms and Genetic Programming - Modern Concepts and Practical Applications. Numerical Insights. CRC Press (2009)Google Scholar
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    Kronberger, G.: Symbolic Regression for Knowledge Discovery - Bloat, Overfitting, and Variable Interaction Networks. Trauner Verlag, Linz (2011)Google Scholar
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    Veeramachaneni, K., Vladislavleva, E., O’Reilly, U.-M.: Knowledge mining sensory evaluation data: genetic programming, statistical techniques, and swarm optimization. Genetic Programming and Evolvable Machines 13(1), 103–133 (2012)CrossRefGoogle Scholar
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    Vladislavleva, E.J., Smits, G.F., den Hertog, D.: Order of nonlinearity as a complexity measure for models generated by symbolic regression via pareto genetic programming. IEEE Transactions on Evolutionary Computation 13(2), 333–349 (2009)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Gabriel Kronberger
    • 1
  • Stefan Wagner
    • 1
  • Michael Kommenda
    • 1
  • Andreas Beham
    • 1
  • Andreas Scheibenpflug
    • 1
  • Michael Affenzeller
    • 1
  1. 1.School for Informatics, Communication and MediaUniversity of Applied Sciences Upper AustriaAustria

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