Embedded Grammars for Grammatical Evolution on GPGPU

  • J. Ignacio HidalgoEmail author
  • Carlos Cervigón
  • J. Manuel Velasco
  • J. Manuel Colmenar
  • Carlos García-Sánchez
  • Guillermo Botella
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10199)


This paper presents an implementation of Grammatical Evolution on a GPU architecture. Our proposal, Embedded Grammars, implements the grammar directly in the code. Although more rigid, it allows to compute the decodification in parallel with the evaluation of the individuals. We tested three different grammars with a set of eight symbolic regression problems. The symbolic regression problems consists on obtaining a mathematical expression in the form \(y=f(x)\), in our case, from a set of 288 pairs xy. The analysis of the results shows that Embedded Grammars are better not only in terms of execution time, but also in quality when compared with an implementation on a CPU. Speed-up results are also better than those presented in the literature.


Grammatical evolution Model identification Symbolic regression Graphic processing unit 



This work was supported by the Spanish Government Minister of Science and Innovation under grants TIN2014-54806-R, TIN2015-65277-R and CAPAP-H5 network (TIN2014-53522) and TIN2015-65460-C2. J.I. Hidalgo also acknowledges the support of the Spanish Ministry of Education mobility grant PRX16/00216.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • J. Ignacio Hidalgo
    • 1
    Email author
  • Carlos Cervigón
    • 1
  • J. Manuel Velasco
    • 1
  • J. Manuel Colmenar
    • 2
  • Carlos García-Sánchez
    • 3
  • Guillermo Botella
    • 3
  1. 1.Adaptive and Bioinspired Systems Research GroupUniversidad Complutense de MadridMadridSpain
  2. 2.Rey Juan Carlos UniversityMstolesSpain
  3. 3.ArTeCS GroupUniversidad Complutense de MadridMadridSpain

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