A comparison between geometric semantic GP and cartesian GP for boolean functions learning

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

  author =       "Andrea Mambrini and Luca Manzoni",
  title =        "A comparison between geometric semantic GP and
                 cartesian GP for boolean functions learning",
  booktitle =    "GECCO Comp '14: Proceedings of the 2014 conference
                 companion on Genetic and evolutionary computation
  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 =        "143--144",
  month =        "12-16 " # jul,
  organisation = "SIGEVO",
  address =      "Vancouver, BC, Canada",
  URL =          "http://doi.acm.org/10.1145/2598394.2598475",
  DOI =          "doi:10.1145/2598394.2598475",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "Geometric Semantic Genetic Programming (GSGP) is a
                 recently defined form of Genetic Programming (GP) that
                 has shown promising results on single output Boolean
                 problems when compared with standard tree-based GP. In
                 this paper we compare GSGP with Cartesian GP (CGP) on
                 comprehensive set of Boolean benchmarks, consisting of
                 both single and multiple outputs Boolean problems. The
                 results obtained show that GSGP outperforms also CGP,
                 confirming the efficacy of GSGP in solving Boolean
  notes =        "Also known as \cite{2598475} Distributed at

Genetic Programming entries for Andrea Mambrini Luca Manzoni