On improving grammatical evolution performance in symbolic regression with attribute grammar

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

  author =       "Muhammad Rezaul Karim and Conor Ryan",
  title =        "On improving grammatical evolution performance in
                 symbolic regression with attribute grammar",
  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, grammatical
                 evolution: Poster",
  pages =        "139--140",
  month =        "12-16 " # jul,
  organisation = "SIGEVO",
  address =      "Vancouver, BC, Canada",
  URL =          "http://doi.acm.org/10.1145/2598394.2598488",
  DOI =          "doi:10.1145/2598394.2598488",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "This paper shows how attribute grammar (AG) can be
                 used with Grammatical Evolution (GE) to avoid
                 invalidators in the symbolic regression solutions
                 generated by GE. In this paper, we also show how
                 interval arithmetic can be implemented with AG to avoid
                 selection of certain arithmetic operators or
                 transcendental functions, whenever necessary to avoid
                 infinite output bounds in the solutions. Results and
                 analysis demonstrate that with the proposed extensions,
                 GE shows significantly less overfitting than standard
                 GE and Koza's GP, on the tested symbolic regression
  notes =        "Also known as \cite{2598488} Distributed at

Genetic Programming entries for Muhammad Rezaul Karim Conor Ryan