Empirical study of surrogate models for black box optimizations obtained using symbolic regression via genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "Glen D. {Rodriguez Rafael} and 
                 Carlos Javier {Solano Salinas}",
  title =        "Empirical study of surrogate models for black box
                 optimizations obtained using symbolic regression via
                 genetic programming",
  booktitle =    "GECCO '11: Proceedings of the 13th annual conference
                 companion on Genetic and evolutionary computation",
  year =         "2011",
  editor =       "Natalio Krasnogor and Pier Luca Lanzi and 
                 Andries Engelbrecht and David Pelta and Carlos Gershenson and 
                 Giovanni Squillero and Alex Freitas and 
                 Marylyn Ritchie and Mike Preuss and Christian Gagne and 
                 Yew Soon Ong and Guenther Raidl and Marcus Gallager and 
                 Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and 
                 Nikolaus Hansen and Silja Meyer-Nieberg and 
                 Jim Smith and Gus Eiben and Ester Bernado-Mansilla and 
                 Will Browne and Lee Spector and Tina Yu and Jeff Clune and 
                 Greg Hornby and Man-Leung Wong and Pierre Collet and 
                 Steve Gustafson and Jean-Paul Watson and 
                 Moshe Sipper and Simon Poulding and Gabriela Ochoa and 
                 Marc Schoenauer and Carsten Witt and Anne Auger",
  isbn13 =       "978-1-4503-0690-4",
  keywords =     "genetic algorithms, genetic programming: Poster",
  pages =        "185--186",
  month =        "12-16 " # jul,
  organisation = "SIGEVO",
  address =      "Dublin, Ireland",
  DOI =          "doi:10.1145/2001858.2001962",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "A black box model is a numerical simulation that is
                 used in optimisation. It is computationally expensive,
                 so it is convenient to replace it with surrogate models
                 obtained by simulating only a few points and then
                 approximating the original black box. Here, a recent
                 approach, using Symbolic Regression via Genetic
                 Programming, is compared experimentally to neural
                 network based surrogate models, using test functions
                 and electromagnetic models. The accuracy of the model
                 obtained by Symbolic Regression is proved to be good,
                 and the interpretability of the function obtained is
                 useful in reducing the optimisation's search space.",
  notes =        "Also known as \cite{2001962} Distributed on CD-ROM at

                 ACM Order Number 910112.",

Genetic Programming entries for Glen D Rodriguez Rafael Carlos Javier Solano Salinas