Using Supportive Coevolution to Evolve Self-Configuring Crossover

Created by W.Langdon from gp-bibliography.bib Revision:1.4496

  author =       "Nathaniel R. Kamrath and Brian W. Goldman and 
                 Daniel R. Tauritz",
  title =        "Using Supportive Coevolution to Evolve
                 Self-Configuring Crossover",
  booktitle =    "GECCO '13 Companion: Proceeding of the fifteenth
                 annual conference companion on Genetic and evolutionary
                 computation conference companion",
  year =         "2013",
  editor =       "Christian Blum and Enrique Alba and 
                 Thomas Bartz-Beielstein and Daniele Loiacono and 
                 Francisco Luna and Joern Mehnen and Gabriela Ochoa and 
                 Mike Preuss and Emilia Tantar and Leonardo Vanneschi and 
                 Kent McClymont and Ed Keedwell and Emma Hart and 
                 Kevin Sim and Steven Gustafson and 
                 Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and 
                 Nikolaus Hansen and Olaf Mersmann and Petr Posik and 
                 Heike Trautmann and Muhammad Iqbal and Kamran Shafi and 
                 Ryan Urbanowicz and Stefan Wagner and 
                 Michael Affenzeller and David Walker and Richard Everson and 
                 Jonathan Fieldsend and Forrest Stonedahl and 
                 William Rand and Stephen L. Smith and Stefano Cagnoni and 
                 Robert M. Patton and Gisele L. Pappa and 
                 John Woodward and Jerry Swan and Krzysztof Krawiec and 
                 Alexandru-Adrian Tantar and Peter A. N. Bosman and 
                 Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and 
                 David L. Gonzalez-Alvarez and 
                 Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and 
                 Kenneth Holladay and Tea Tusar and Boris Naujoks",
  isbn13 =       "978-1-4503-1964-5",
  keywords =     "genetic algorithms, genetic programming",
  pages =        "1489--1496",
  month =        "6-10 " # jul,
  organisation = "SIGEVO",
  address =      "Amsterdam, The Netherlands",
  URL =          "",
  DOI =          "doi:10.1145/2464576.2482727",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "Creating an Evolutionary Algorithm (EA) which is
                 capable of automatically configuring itself and
                 dynamically controlling its parameters is a challenging
                 problem. However, solving this problem can reduce the
                 amount of manual configuration required to implement an
                 EA, allow the EA to be more adaptable, and produce
                 better results on a range of problems without requiring
                 problem specific tuning. Using Supportive Coevolution
                 (SuCo) to evolve Self-Configuring Crossover (SCX)
                 combines the automatic configuration technique of
                 multiple populations from SuCo with the dynamic
                 crossover operator creation and evolution of SCX.

                 This paper reports an empirical comparison and analysis
                 of several different combinations of mutation and
                 crossover techniques including SuCo and SCX. The
                 Rosenbrock, Rastrigin, and Offset Rastrigin benchmark
                 problems were selected for testing purposes. The
                 benefits and drawbacks of self-adaptation and evolution
                 of SCX are also discussed. SuCo of mutation step sizes
                 and SCX operators produced results that were at least
                 as good as previous work, and some experiments produced
                 results that were significantly better.",
  notes =        "Also known as \cite{2482727} Distributed at

Genetic Programming entries for Nathaniel R Kamrath Brian W Goldman Daniel R Tauritz