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Genetic Programming Model Regularization

  • 2016
  • OriginalPaper
  • Chapter
Published in:
Authors
César L. Alonso
José Luis Montaña
Cruz Enrique Borges
Published in
Computational Intelligence
Publisher
Springer International Publishing

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Abstract

We propose a tool for controlling the complexity of Genetic Programming models. The tool is supported by the theory of Vapnik-Chervonekis dimension (VCD) and is combined with a novel representation of models named straight line program. Experimental results, implemented on conventional algebraic structures (such as polynomials), show that the empirical risk, penalized by suitable upper bounds for the Vapnik-Chervonenkis dimension, gives a generalization error smaller than the use of statistical conventional techniques such as Bayesian or Akaike information criteria.

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previous chapter A Targeted Estimation of Distribution Algorithm Compared to Traditional Methods in Feature Selection
next chapter A Radial Basis Function Neural Network-Based Coevolutionary Algorithm for Short-Term to Long-Term Time Series Forecasting
Title
Genetic Programming Model Regularization
Authors
César L. Alonso
José Luis Montaña
Cruz Enrique Borges
Copyright Year
2016
Publisher
Springer International Publishing
Book
Computational Intelligence

Print ISBN: 978-3-319-23391-8

Electronic ISBN: 978-3-319-23392-5

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-23392-5_6
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