Structural difficulty in grammatical evolution versus genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4192

  author =       "Ann Thorhauer and Franz Rothlauf",
  title =        "Structural difficulty in grammatical evolution versus
                 genetic programming",
  booktitle =    "GECCO '13: Proceeding of the fifteenth annual
                 conference on Genetic and evolutionary computation
  year =         "2013",
  editor =       "Christian Blum and Enrique Alba and Anne Auger and 
                 Jaume Bacardit and Josh Bongard and Juergen Branke and 
                 Nicolas Bredeche and Dimo Brockhoff and 
                 Francisco Chicano and Alan Dorin and Rene Doursat and 
                 Aniko Ekart and Tobias Friedrich and Mario Giacobini and 
                 Mark Harman and Hitoshi Iba and Christian Igel and 
                 Thomas Jansen and Tim Kovacs and Taras Kowaliw and 
                 Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and 
                 John McCall and Alberto Moraglio and 
                 Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and 
                 Gustavo Olague and Yew-Soon Ong and 
                 Michael E. Palmer and Gisele Lobo Pappa and 
                 Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and 
                 Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and 
                 Daniel Tauritz and Leonardo Vanneschi",
  isbn13 =       "978-1-4503-1963-8",
  pages =        "997--1004",
  keywords =     "genetic algorithms, genetic programming, grammatical
  month =        "6-10 " # jul,
  organisation = "SIGEVO",
  address =      "Amsterdam, The Netherlands",
  DOI =          "doi:10.1145/2463372.2463491",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "Genetic programming (GP) has problems with structural
                 difficulty as it is unable to search effectively for
                 solutions requiring very full or very narrow trees. As
                 a result of structural difficulty, GP has a bias
                 towards narrow trees which means it searches
                 effectively for solutions requiring narrow trees. This
                 paper focuses on the structural difficulty of
                 grammatical evolution (GE). In contrast to GP, GE works
                 on variable-length binary strings and uses a grammar in
                 Backus-Naur Form (BNF) to map linear genotypes to
                 phenotype trees. The paper studies whether and how GE
                 is affected by structural difficulty. For the analysis,
                 we perform random walks through the search space and
                 compare the structure of the visited solutions. In
                 addition, we compare the performance of GE and GP for
                 the Lid problem. Results show that GE representation is
                 biased, this means it has problems with structural
                 difficulty. For binary trees, GE has a bias towards
                 narrow and deep structures; thus GE outperforms
                 standard GP if optimal solutions are composed of very
                 narrow and deep structures. In contrast, problems where
                 optimal solutions require more dense trees are easier
                 to solve for GP than for GE.",
  notes =        "Also known as \cite{2463491} GECCO-2013 A joint
                 meeting of the twenty second international conference
                 on genetic algorithms (ICGA-2013) and the eighteenth
                 annual genetic programming conference (GP-2013)",

Genetic Programming entries for Ann Thorhauer Franz Rothlauf