Evolving black-box search algorithms employing genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4394

  author =       "Matthew A. Martin and Daniel R. Tauritz",
  title =        "Evolving black-box search algorithms employing genetic
  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 =        "1497--1504",
  month =        "6-10 " # jul,
  organisation = "SIGEVO",
  address =      "Amsterdam, The Netherlands",
  DOI =          "doi:10.1145/2464576.2482728",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "Restricting the class of problems we want to perform
                 well on allows Black Box Search Algorithms (BBSAs)
                 specifically tailored to that class to significantly
                 outperform more general purpose problem solvers.
                 However, the fields that encompass BBSAs, including
                 Evolutionary Computing, are mostly focused on improving
                 algorithm performance over increasingly diversified
                 problem classes. By definition, the payoff for
                 designing a high quality general purpose solver is far
                 larger in terms of the number of problems it can
                 address, than a specialised BBSA. This paper introduces
                 a novel approach to creating tailored BBSAs through
                 automated design employing genetic programming. An
                 experiment is reported which demonstrates its ability
                 to create novel BBSAs which outperform established
                 BBSAs including canonical evolutionary algorithms.",
  notes =        "Also known as \cite{2482728} Distributed at

Genetic Programming entries for Matthew A Martin Daniel R Tauritz