Recurrent Cartesian Genetic Programming Applied to Series Forecasting

Created by W.Langdon from gp-bibliography.bib Revision:1.4549

  author =       "Andrew James Turner and Julian Francis Miller",
  title =        "Recurrent Cartesian Genetic Programming Applied to
                 Series Forecasting",
  booktitle =    "GECCO Companion '15: Proceedings of the Companion
                 Publication of the 2015 Annual Conference on Genetic
                 and Evolutionary Computation",
  year =         "2015",
  editor =       "Sara Silva and Anna I Esparcia-Alcazar and 
                 Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and 
                 Christine Zarges and Luis Correia and Terence Soule and 
                 Mario Giacobini and Ryan Urbanowicz and 
                 Youhei Akimoto and Tobias Glasmachers and 
                 Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and 
                 Marta Soto and Carlos Cotta and Francisco B. Pereira and 
                 Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and 
                 Heike Trautmann and Jean-Baptiste Mouret and 
                 Sebastian Risi and Ernesto Costa and Oliver Schuetze and 
                 Krzysztof Krawiec and Alberto Moraglio and 
                 Julian F. Miller and Pawel Widera and Stefano Cagnoni and 
                 JJ Merelo and Emma Hart and Leonardo Trujillo and 
                 Marouane Kessentini and Gabriela Ochoa and Francisco Chicano and 
                 Carola Doerr",
  isbn13 =       "978-1-4503-3488-4",
  keywords =     "genetic algorithms, genetic programming, cartesian
                 genetic programming: Poster",
  pages =        "1499--1500",
  month =        "11-15 " # jul,
  organisation = "SIGEVO",
  address =      "Madrid, Spain",
  URL =          "",
  DOI =          "doi:10.1145/2739482.2764647",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "Recurrent Cartesian Genetic Programming is a recently
                 proposed extension to Cartesian Genetic Programming
                 which allows cyclic program structures to be evolved.
                 We apply both standard and Recurrent Cartesian Genetic
                 Programming to the domain of series forecasting. Their
                 performance is then compared to a number of well-known
                 classical forecasting approaches. Our results show that
                 not only does Recurrent Cartesian Genetic Programming
                 outperform standard Cartesian Genetic Programming, but
                 it also outperforms many standard forecasting
  notes =        "Also known as \cite{2764647} Distributed at

Genetic Programming entries for Andrew James Turner Julian F Miller