Comparing methods for module identification in grammatical evolution

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  author =       "John Swafford and Miguel Nicolau and Erik Hemberg and 
                 Michael O'Neill and Anthony Brabazon",
  title =        "Comparing methods for module identification in
                 grammatical evolution",
  booktitle =    "GECCO '12: Proceedings of the fourteenth international
                 conference on Genetic and evolutionary computation
  year =         "2012",
  editor =       "Terry Soule and Anne Auger and Jason Moore and 
                 David Pelta and Christine Solnon and Mike Preuss and 
                 Alan Dorin and Yew-Soon Ong and Christian Blum and 
                 Dario Landa Silva and Frank Neumann and Tina Yu and 
                 Aniko Ekart and Will Browne and Tim Kovacs and 
                 Man-Leung Wong and Clara Pizzuti and Jon Rowe and Tobias Friedrich and 
                 Giovanni Squillero and Nicolas Bredeche and 
                 Stephen L. Smith and Alison Motsinger-Reif and Jose Lozano and 
                 Martin Pelikan and Silja Meyer-Nienberg and 
                 Christian Igel and Greg Hornby and Rene Doursat and 
                 Steve Gustafson and Gustavo Olague and Shin Yoo and 
                 John Clark and Gabriela Ochoa and Gisele Pappa and 
                 Fernando Lobo and Daniel Tauritz and Jurgen Branke and 
                 Kalyanmoy Deb",
  isbn13 =       "978-1-4503-1177-9",
  pages =        "823--830",
  keywords =     "genetic algorithms, genetic programming",
  month =        "7-11 " # jul,
  organisation = "SIGEVO",
  address =      "Philadelphia, Pennsylvania, USA",
  DOI =          "doi:10.1145/2330163.2330277",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "Modularity has been an important vein of research in
                 evolutionary algorithms. Past research in evolutionary
                 computation has shown that techniques able to decompose
                 the benchmark problems examined in this work into
                 smaller, more easily solved, sub-problems have an
                 advantage over those which do not. This work describes
                 and analyzes a number of approaches to discover
                 sub-solutions (modules) in the grammatical evolution
                 algorithm. Data from the experiments carried out show
                 that particular approaches to identifying modules are
                 better suited to certain problem types, at varying
                 levels of difficulty. The results presented here show
                 that some of these approaches are able to significantly
                 outperform standard grammatical evolution and
                 grammatical evolution using automatically defined
                 functions on a subset of the problems tested. The
                 results also point to a number of possibilities for
                 extending this work to further enhance approaches to
  notes =        "Also known as \cite{2330277} GECCO-2012 A joint
                 meeting of the twenty first international conference on
                 genetic algorithms (ICGA-2012) and the seventeenth
                 annual genetic programming conference (GP-2012)",

Genetic Programming entries for John Mark Swafford Miguel Nicolau Erik Hemberg Michael O'Neill Anthony Brabazon