Examining the "Best of Both Worlds" of Grammatical Evolution

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

  author =       "Peter A. Whigham and Grant Dick and 
                 James Maclaurin and Caitlin A. Owen",
  title =        "Examining the {"}Best of Both Worlds{"} of Grammatical
  booktitle =    "GECCO '15: Proceedings 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 =       "978-1-4503-3472-3",
  pages =        "1111--1118",
  keywords =     "genetic algorithms, genetic programming, grammatical
  month =        "11-15 " # jul,
  organisation = "SIGEVO",
  address =      "Madrid, Spain",
  URL =          "http://doi.acm.org/10.1145/2739480.2754784",
  DOI =          "doi:10.1145/2739480.2754784",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "Grammatical Evolution (GE) has a long history in
                 evolutionary computation. Central to the behaviour of
                 GE is the use of a linear representation and grammar to
                 map individuals from search spaces into problem spaces.
                 This genotype to phenotype mapping is often argued as a
                 distinguishing property of GE relative to other
                 techniques, such as context-free grammar genetic
                 programming (CFG-GP). Since its initial description, GE
                 research has attempted to incorporate information from
                 the grammar into crossover, mutation, and individual
                 initialisation, blurring the distinction between
                 genotype and phenotype and creating GE variants closer
                 to CFG-GP. This is argued to provide GE with the best
                 of both worlds, allowing degrees of grammatical bias to
                 be introduced into operators to best suit the given
                 problem. This paper examines the behaviour of three
                 grammar-based search methods on several problems from
                 previous GE research. It is shown that, unlike CFG-GP,
                 the performance of pure GE on the examined problems
                 closely resembles that of random search. The results
                 suggest that further work is required to determine the
                 cases where the best of both worlds of GE are required
                 over a straight CFG-GP approach.",
  notes =        "Also known as \cite{2754784} GECCO-2015 A joint
                 meeting of the twenty fourth international conference
                 on genetic algorithms (ICGA-2015) and the twentith
                 annual genetic programming conference (GP-2015)",

Genetic Programming entries for Peter Alexander Whigham Grant Dick James Maclaurin Caitlin A Owen