Coevolution in Cartesian Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Michaela Sikulova and Lukas Sekanina",
  title =        "Coevolution in Cartesian Genetic Programming",
  booktitle =    "Proceedings of the 15th European Conference on Genetic
                 Programming, EuroGP 2012",
  year =         "2012",
  month =        "11-13 " # apr,
  editor =       "Alberto Moraglio and Sara Silva and 
                 Krzysztof Krawiec and Penousal Machado and Carlos Cotta",
  series =       "LNCS",
  volume =       "7244",
  publisher =    "Springer Verlag",
  address =      "Malaga, Spain",
  pages =        "182--193",
  organisation = "EvoStar",
  isbn13 =       "978-3-642-29138-8",
  DOI =          "doi:10.1007/978-3-642-29139-5_16",
  size =         "12 pages",
  keywords =     "genetic algorithms, genetic programming, cartesian
                 genetic programming, Coevolution, Symbolic regression",
  abstract =     "Cartesian genetic programming (CGP) is a branch of
                 genetic programming which has been used in various
                 applications. This paper proposes to introduce
                 coevolution to CGP in order to accelerate the task of
                 symbolic regression. In particular, fitness predictors
                 which are small subsets of the training set are
                 coevolved with CGP programs. It is shown using five
                 symbolic regression problems that the (median)
                 execution time can be reduced 2--5 times in comparison
                 with the standard CGP.",
  notes =        "1+lambda hill climber with point mutation on CGP
                 representation with restart. 1pt crossover?

                 Part of \cite{Moraglio:2012:GP} EuroGP'2012 held in
                 conjunction with EvoCOP2012 EvoBIO2012, EvoMusArt2012
                 and EvoApplications2012",

Genetic Programming entries for Michaela Sikulova Lukas Sekanina