Symbolic Regression via Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

  author =       "Douglas A. Augusto and Helio J. C. Barbosa",
  title =        "Symbolic Regression via Genetic Programming",
  booktitle =    "{VI} Brazilian Symposium on Neural Networks
  year =         "2000",
  pages =        "173",
  address =      "Rio de Janeiro, RJ, Brazil",
  month =        jan # " 22-25",
  note =         "VI Simposio Brasileiro de Redes Neurais",
  keywords =     "genetic algorithms, genetic programming",
  identifier =   "sbrn2000article029",
  language =     "eng",
  source =       "sbrn2000",
  URL =          "",
  DOI =          "doi:10.1109/SBRN.2000.889734",
  abstract =     "In this work, we present an implementation of symbolic
                 regression, which is based on genetic programming (GP).
                 Unfortunately, standard implementations of GP in
                 compiled languages are not usually the most efficient
                 ones. The present approach employs a simple
                 representation for tree-like structures by making use
                 of Read's linear code, leading to more simplicity and
                 better performance when compared with traditional GP
                 implementations. Creation, crossover and mutation of
                 individuals are formalized. An extension allowing for
                 the creation of random coefficients is presented. The
                 efficiency of the proposed implementation was confirmed
                 in computational experiments, which are summarized in
                 this paper.",

Genetic Programming entries for Douglas A Augusto Helio J C Barbosa