Comparison of linear genetic programming variants for symbolic regression

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

@InProceedings{Sotto:2014:GECCOcomp,
  author =       "Leo Francoso Dal Piccol Sotto and 
                 Vinicius Veloso {de Melo}",
  title =        "Comparison of linear genetic programming variants for
                 symbolic regression",
  booktitle =    "GECCO Comp '14: Proceedings of the 2014 conference
                 companion on Genetic and evolutionary computation
                 companion",
  year =         "2014",
  editor =       "Christian Igel and Dirk V. Arnold and 
                 Christian Gagne and Elena Popovici and Anne Auger and 
                 Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and 
                 Kalyanmoy Deb and Benjamin Doerr and James Foster and 
                 Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and 
                 Hitoshi Iba and Christian Jacob and Thomas Jansen and 
                 Yaochu Jin and Marouane Kessentini and 
                 Joshua D. Knowles and William B. Langdon and Pedro Larranaga and 
                 Sean Luke and Gabriel Luque and John A. W. McCall and 
                 Marco A. {Montes de Oca} and Alison Motsinger-Reif and 
                 Yew Soon Ong and Michael Palmer and 
                 Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and 
                 Guenther Ruhe and Tom Schaul and Thomas Schmickl and 
                 Bernhard Sendhoff and Kenneth O. Stanley and 
                 Thomas Stuetzle and Dirk Thierens and Julian Togelius and 
                 Carsten Witt and Christine Zarges",
  isbn13 =       "978-1-4503-2881-4",
  keywords =     "genetic algorithms, genetic programming: Poster",
  pages =        "135--136",
  month =        "12-16 " # jul,
  organisation = "SIGEVO",
  address =      "Vancouver, BC, Canada",
  URL =          "http://doi.acm.org/10.1145/2598394.2598472",
  DOI =          "doi:10.1145/2598394.2598472",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "In this paper, we compare a basic linear genetic
                 programming (LGP) algorithm against several LGP
                 variants, proposed by us, on two sets of symbolic
                 regression benchmarks. We evaluated the influence of
                 methods to control bloat, investigated these techniques
                 focused in growth of effective code, and examined an
                 operator to consider two successful individuals as
                 modules to be integrated into a new individual. Results
                 suggest that methods that deal with program size,
                 percentage of effective code, and subfunctions, can
                 improve the quality of the final solutions.",
  notes =        "Also known as \cite{2598472} Distributed at
                 GECCO-2014.",
}

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

Citations