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Improving Symbolic Regression with Interval Arithmetic and Linear Scaling

  • Maarten Keijzer
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2610)

Abstract

The use of protected operators and squared error measures are standard approaches in symbolic regression. It will be shown that two relatively minor modifications of a symbolic regression system can result in greatly improved predictive performance and reliability of the induced expressions. To achieve this, interval arithmetic and linear scaling are used. An experimental section demonstrates the improvements on 15 symbolic regression problems.

Keywords

Mean Square Error Genetic Programming Interval Arithmetic Linear Scaling Normalize Root Mean Square Error 
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 2003

Authors and Affiliations

  • Maarten Keijzer
    • 1
  1. 1.Computer Science DepartmentFree University AmsterdamAmsterdam

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