Adaptation, Performance and Vapnik-Chervonenkis Dimension of Straight Line Programs

  • José L. Montaña
  • César L. Alonso
  • Cruz E. Borges
  • José L. Crespo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5481)


We discuss here empirical comparation between model selection methods based on Linear Genetic Programming. Two statistical methods are compared: model selection based on Empirical Risk Minimization (ERM) and model selection based on Structural Risk Minimization (SRM). For this purpose we have identified the main components which determine the capacity of some linear structures as classifiers showing an upper bound for the Vapnik-Chervonenkis (VC) dimension of classes of programs representing linear code defined by arithmetic computations and sign tests. This upper bound is used to define a fitness based on VC regularization that performs significantly better than the fitness based on empirical risk.


Genetic Programming Linear Genetic Programming Vapnik-Chervonenkis dimension 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • José L. Montaña
    • 1
  • César L. Alonso
    • 2
  • Cruz E. Borges
    • 1
  • José L. Crespo
    • 3
  1. 1.Departamento de Matemáticas, Estadística y ComputaciónUniversidad de CantabriaSantanderSpain
  2. 2.Centro de Inteligencia ArtificialUniversidad de OviedoGijónSpain
  3. 3.Departamento de Matemática Aplicada, Estadística y Ciencias de la ComputaciónUniversidad de CantabriaSantanderSpain

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