Genetic Programming with Linear Representation: a Survey

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@Article{journals/ijait/OlteanGDM09,
  title =        "Genetic Programming with Linear Representation: a
                 Survey",
  author =       "Mihai Oltean and Crina Grosan and Laura Diosan and 
                 Cristina Mihaila",
  journal =      "International Journal on Artificial Intelligence
                 Tools",
  year =         "2009",
  number =       "2",
  volume =       "18",
  pages =        "197--238",
  keywords =     "genetic algorithms, genetic programming, cartesian
                 genetic programming, linear genetic programming, gene
                 expression programming, multi expression programming,
                 grammatical evolution, stack-based genetic
                 programming",
  DOI =          "doi:10.1142/S0218213009000111",
  bibdate =      "2009-09-23",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/db/journals/ijait/ijait18.html#OlteanGDM09",
  abstract =     "Genetic Programming (GP) is an automated method for
                 creating computer programs starting from a high-level
                 description of the problem to be solved. Many variants
                 of GP have been proposed in the recent years. In this
                 paper we are reviewing the main GP variants with linear
                 representation. Namely, Linear Genetic Programming,
                 Gene Expression Programming, Multi Expression
                 Programming, Grammatical Evolution, Cartesian Genetic
                 Programming and Stack-Based Genetic Programming. A
                 complete description is provided for each method. The
                 set of applications where the methods have been applied
                 and several Internet sites with more information about
                 them are also given.",
}

Genetic Programming entries for Mihai Oltean Crina Grosan Laura Diosan Cristina Mihaila

Citations