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Controlling Overfitting in Symbolic Regression Based on a Bias/Variance Error Decomposition

  • Alexandros Agapitos
  • Anthony Brabazon
  • Michael O’Neill
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7491)

Abstract

We consider the fundamental property of generalisation of data-driven models evolved by means of Genetic Programming (GP). The statistical treatment of decomposing the regression error into bias and variance terms provides insight into the generalisation capability of this modelling method. The error decomposition is used as a source of inspiration to design a fitness function that relaxes the sensitivity of an evolved model to a particular training dataset. Results on eight symbolic regression problems show that new method is capable on inducing better-generalising models than standard GP for most of the problems.

Keywords

Genetic Programming Training Dataset Regression Error Symbolic Regression Genetic Programming System 
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 2012

Authors and Affiliations

  • Alexandros Agapitos
    • 1
  • Anthony Brabazon
    • 1
  • Michael O’Neill
    • 1
  1. 1.Financial Mathematics and Computation Research Cluster, Natural Computing Research and Applications GroupUniversity College DublinIreland

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