Dynamic environments can speed up evolution with genetic programming

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

  author =       "Michael O'Neill and Miguel Nicolau and 
                 Anthony Brabazon",
  title =        "Dynamic environments can speed up evolution with
                 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, grammatical
                 evolution: Poster",
  pages =        "191--192",
  month =        "12-16 " # jul,
  organisation = "SIGEVO",
  address =      "Dublin, Ireland",
  DOI =          "doi:10.1145/2001858.2001965",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "We present a study of dynamic environments with
                 genetic programming to ascertain if a dynamic
                 environment can speed up evolution when compared to an
                 equivalent static environment. We present an analysis
                 of the types of dynamic variation which can occur with
                 a variable-length representation such as adopted in
                 genetic programming identifying modular varying,
                 structural varying and incremental varying goals. An
                 empirical investigation comparing these three types of
                 varying goals on dynamic symbolic regression benchmarks
                 reveals an advantage for goals which vary in terms of
                 increasing structural complexity. This provides
                 evidence to support the added difficulty variable
                 length representations incur due to their requirement
                 to search structural and parametric space concurrently,
                 and how directing search through varying structural
                 goals with increasing complexity can speed up search
                 with genetic programming.",
  notes =        "Also known as \cite{2001965} Distributed on CD-ROM at

                 ACM Order Number 910112.",

Genetic Programming entries for Michael O'Neill Miguel Nicolau Anthony Brabazon