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Solving the Unknown Complexity Formula Problem with Genetic Programming

  • Rayco Batista
  • Eduardo Segredo
  • Carlos Segura
  • Coromoto León
  • Casiano Rodríguez
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7902)

Abstract

The Unknown Complexity Formula Problem (ucfp) is a particular case of the symbolic regression problem in which an analytical complexity formula that fits with data obtained by multiple executions of certain algorithm must be given. In this work, a set of modifications has been added to the standard Genetic Programming (gp) algorithm to deal with the ucfp. This algorithm has been applied to a set of well-known benchmark functions of the symbolic regression problem. Moreover, a real case of the ucfp has been tackled. Experimental evaluation has demonstrated the good behaviour of the proposed approach in obtaining high quality solutions, even for a real instance of the ucfp. Finally, it is worth pointing out that the best published results for the majority of benchmark functions have been improved.

Keywords

Genetic Programming Symbolic Regression Unknown Complexity Formula Problem 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Rayco Batista
    • 1
  • Eduardo Segredo
    • 1
  • Carlos Segura
    • 1
  • Coromoto León
    • 1
  • Casiano Rodríguez
    • 1
  1. 1.Dpto. Estadística, I. O. y ComputaciónUniversidad de La LagunaLa LagunaSpain

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