Investigation of Linear Genetic Programming Techniques for Symbolic Regression

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

  author =       "Leo Francoso Dal Piccol Sotto and 
                 Vinicius Veloso {de Melo}",
  title =        "Investigation of Linear Genetic Programming Techniques
                 for Symbolic Regression",
  booktitle =    "Brazilian Conference on Intelligent Systems, BRACIS
  year =         "2014",
  editor =       "Ricardo B. C. Prudencio and Paulo E. Santos",
  pages =        "146--151",
  address =      "Sao Paulo, Brazil",
  month =        oct # " 18-22",
  organisation = "SBC",
  publisher =    "IEEE",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-1-4799-5618-0",
  timestamp =    "Sat, 04 Oct 4429704 11:57:20 +",
  biburl =       "",
  bibsource =    "dblp computer science bibliography,",
  URL =          "",
  URL =          "",
  DOI =          "doi:10.1109/BRACIS.2014.36",
  size =         "6 pages",
  abstract =     "In this paper, we investigate some variants of a basic
                 linear genetic programming (LGP) algorithm in the
                 problem of symbolic regression. We explore the effects
                 of using techniques to control bloat and to privilege a
                 greater percentage of effective code in the population,
                 individually, and examine its possibility of producing
                 better solutions. We also test the effects and
                 performance of an operator that considers two
                 successful individuals as sub functions and join them
                 into a new individual. We conduct experiments and
                 discuss what effects each variant introduces to the
                 evolution and its chance of producing better
  notes =        "",

Genetic Programming entries for Leo Francoso Dal Piccol Sotto Vinicius Veloso de Melo