Stateful program representations for evolving technical trading rules

Created by W.Langdon from gp-bibliography.bib Revision:1.2031

@InProceedings{Agapitos:2011:GECCOcomp,
  author =       "Alexandros Agapitos and Michael O'Neill and 
                 Anthony Brabazon",
  title =        "Stateful program representations for evolving
                 technical trading rules",
  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 =        "199--200",
  month =        "12-16 " # jul,
  organisation = "SIGEVO",
  address =      "Dublin, Ireland",
  doi =          "doi:10.1145/2001858.2001969",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "A family of stateful program representations in
                 grammar-based Genetic Programming are being compared
                 against their stateless counterpart in the problem of
                 binary classification of sequences of daily prices of a
                 financial asset. Empirical results suggest that
                 stateful classifiers learn as fast as stateless ones
                 but generalise better to unseen data, rendering this
                 form of program representation strongly appealing to
                 the automatic programming of technical trading rules.",
  notes =        "Also known as \cite{2001969} Distributed on CD-ROM at
                 GECCO-2011.

                 ACM Order Number 910112.",
}

Genetic Programming entries for Alexandros Agapitos Michael O'Neill Anthony Brabazon