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Symbolic Regression by Means of Grammatical Evolution with Estimation Distribution Algorithms as Search Engine

  • M. A. Sotelo-FigueroaEmail author
  • Arturo Hernández-Aguirre
  • Andrés Espinal
  • J. A. Soria-Alcaraz
  • Janet Ortiz-López
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 749)

Abstract

Grammatical Evolution (GE) is a Grammar-based form of Genetic Programming (GP) and it has been used to evolve programs or rules. The GE uses a population of linear genotypic strings and it is transformed by mapping process, those string are evolved using a search engine like the Genetic Algorithm (GA), Differential Evolution (DE), Particle Swarm Optimization (PSO), among others. One of the big trouble of these algorithms is the parameter tuning. In this paper is proposed an Estimation Distribution Algorithm (EDA) as search engine using the Symbolic Regression as a benchmark, due to the few parameters used by the EDA. The results were compared against the obtained by DE as search engine using the Friedman nonparametric test.

Keywords

Grammatical Evolution Estimation Distribution Algorithm Symbolic Regression 

Notes

Acknowledgements

The authors want to thank to Universidad de Guanajuato (UG) for the support to this research.

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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • M. A. Sotelo-Figueroa
    • 1
    Email author
  • Arturo Hernández-Aguirre
    • 2
  • Andrés Espinal
    • 1
  • J. A. Soria-Alcaraz
    • 1
  • Janet Ortiz-López
    • 3
  1. 1.División de Ciencias Económico Administrativas, Departamento de Estudios OrganizacionalesUniversidad de GuanajuatoGuanajuatoMexico
  2. 2.Departamento de Ciencias de La ComputaciónCentro de Investigación En MatemáticasGuanajuatoMexico
  3. 3.Escuela Internacional de DoctoradoUniversidad de VigoVigoSpain

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