Grammar-Based Immune Programming for Symbolic Regression

  • Authors:
  • Heder S. Bernardino;Helio J. Barbosa

  • Affiliations:
  • Laboratório Nacional de Computação Científica, Petrópolis, Brazil 25.651-075;Laboratório Nacional de Computação Científica, Petrópolis, Brazil 25.651-075

  • Venue:
  • ICARIS '09 Proceedings of the 8th International Conference on Artificial Immune Systems
  • Year:
  • 2009

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Abstract

This paper presents a Grammar-based Immune Programming (GIP) that can evolve programs in an arbitrary language using a clonal selection algorithm. A context-free grammar that defines this language is used to decode candidate programs (antibodies) to a valid representation. The programs are represented by tree data structures as the majority of the program evolution algorithms do. The GIP is applied to symbolic regression problems and the results found show that it is competitive when compared with other algorithms from the literature.