Gene Expression Programming: A New Adaptive Algorithm for Solving Problems

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

@Article{ferreira:2001:CS,
  author =       "C\^andida Ferreira",
  title =        "Gene Expression Programming: A New Adaptive Algorithm
                 for Solving Problems",
  journal =      "Complex Systems",
  year =         "2001",
  volume =       "13",
  number =       "2",
  pages =        "87--129",
  email =        "candidaf@gene-expressionprogramming.com",
  keywords =     "genetic algorithms, genetic programming, GEP",
  URL =          "http://www.gene-expression-programming.com/webpapers/GEPfirst.pdf",
  URL =          "http://www.complex-systems.com/abstracts/v13_i02_a01.html",
  URL =          "http://arXiv.org/abs/cs/0102027",
  abstract =     "Gene expression programming, a genotype/phenotype
                 genetic algorithm (linear and ramified), is presented
                 here for the first time as a new technique for the
                 creation of computer programs. Gene expression
                 programming uses character linear chromosomes composed
                 of genes structurally organized in a head and a tail.
                 The chromosomes function as a genome and are subjected
                 to modification by means of mutation, transposition,
                 root transposition, gene transposition, gene
                 recombination, and one- and two-point recombination.
                 The chromosomes encode expression trees which are the
                 object of selection. The creation of these separate
                 entities (genome and expression tree) with distinct
                 functions allows the algorithm to perform with high
                 efficiency that greatly surpasses existing adaptive
                 techniques. The suite of problems chosen to illustrate
                 the power and versatility of gene expression
                 programming includes symbolic regression, sequence
                 induction with and without constant creation, block
                 stacking, cellular automata rules for the
                 density-classification problem, and two problems of
                 boolean concept learning: the 11-multiplexer and the GP
                 rule problem.",
  notes =        "Portuguese translation
                 http://www.gene-expression-programming.com/webpapers/GEPPort.pdf",
}

Genetic Programming entries for Candida Ferreira

Citations