- 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
GECCO-2011.
ACM Order Number 910112.",
