Grammar-Based Immune Programming for Symbolic Regression

Created by W.Langdon from gp-bibliography.bib Revision:1.4549

  author =       "Heder S. Bernardino and Helio J. C. Barbosa",
  title =        "Grammar-Based Immune Programming for Symbolic
  booktitle =    "Proceedings of the 8th International Conference on
                 Artificial Immune Systems (ICARIS)",
  year =         "2009",
  editor =       "Paul S. Andrews and Jon Timmis and 
                 Nick D. L. Owens and Uwe Aickelin and Emma Hart and Andrew Hone and 
                 Andy M. Tyrrell",
  volume =       "5666",
  series =       "Lecture Notes in Computer Science",
  pages =        "274--287",
  address =      "York, UK",
  month =        aug # " 9-12",
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming, grammatical
                 evolution, Artificial immune system, immune
                 programming, symbolic regression",
  isbn13 =       "978-3-642-03245-5",
  language =     "English",
  URL =          "",
  DOI =          "doi:10.1007/978-3-642-03246-2_26",
  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.",

Genetic Programming entries for Heder Soares Bernardino Helio J C Barbosa