Artificial Immune System Programming for Symbolic Regression

Created by W.Langdon from gp-bibliography.bib Revision:1.4340

  author =       "Colin G. Johnson",
  title =        "Artificial Immune System Programming for Symbolic
  booktitle =    "Genetic Programming, Proceedings of EuroGP'2003",
  year =         "2003",
  editor =       "Conor Ryan and Terence Soule and Maarten Keijzer and 
                 Edward Tsang and Riccardo Poli and Ernesto Costa",
  volume =       "2610",
  series =       "LNCS",
  pages =        "345--353",
  address =      "Essex",
  publisher_address = "Berlin",
  month =        "14-16 " # apr,
  organisation = "EvoNet",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming: Poster",
  ISBN =         "3-540-00971-X",
  URL =          "",
  URL =          "",
  DOI =          "doi:10.1007/3-540-36599-0_32",
  abstract =     "Artificial Immune Systems are computational algorithms
                 which take their inspiration from the way in which
                 natural immune systems learn to respond to attacks on
                 an organism. This paper discusses how such a system can
                 be used as an alternative to genetic algorithms as a
                 way of exploring program-space in a system similar to
                 genetic programming. Some experimental results are
                 given for a symbolic regression problem. The paper ends
                 with a discussion of future directions for the use of
                 artificial immune systems in program induction.",
  notes =        "EuroGP'2003 held in conjunction with EvoWorkshops

Genetic Programming entries for Colin G Johnson