Automated Discovery of Polynomials by Inductive Genetic Programming

  • Nikolay Nikolaev
  • Hitoshi Iba
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1704)


This paper presents an approach to automated discovery of high-order multivariate polynomials by inductive Genetic Programming (iGP). Evolutionary search is used for learning polynomials represented as non-linear multivariate trees. Optimal search performance is pursued with balancing the statistical bias and the variance of iGP. We reduce the bias by extending the set of basis polynomials for better agreement with the examples. Possible overfitting due to the reduced bias is conteracted by a variance component, implemented as a regularizing factor of the error in an MDL fitness function. Experimental results demonstrate that regularized iGP discovers accurate, parsimonious, and predictive polynomials when trained on practical data mining tasks.


Genetic Program Statistical Bias Basis Polynomial Automate Discovery Data Mining Task 
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 1999

Authors and Affiliations

  • Nikolay Nikolaev
    • 1
  • Hitoshi Iba
    • 2
  1. 1.Department of Computer ScienceAmerican University in BulgariaBlagoevgradBulgaria
  2. 2.Department of Information and Communication Engineering, School of EngineeringThe University of TokyoTokyoJapan

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