A Grammatical Genetic Programming Approach to Modularity in Genetic Algorithms

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

  author =       "Erik Hemberg and Conor Gilligan and 
                 Michael O'Neill and Anthony Brabazon",
  title =        "A Grammatical Genetic Programming Approach to
                 Modularity in Genetic Algorithms",
  editor =       "Marc Ebner and Michael O'Neill and Anik\'o Ek\'art and 
                 Leonardo Vanneschi and Anna Isabel Esparcia-Alc\'azar",
  booktitle =    "Proceedings of the 10th European Conference on Genetic
  publisher =    "Springer",
  series =       "Lecture Notes in Computer Science",
  volume =       "4445",
  year =         "2007",
  address =      "Valencia, Spain",
  month =        "11-13 " # apr,
  pages =        "1--11",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-71602-5",
  isbn13 =       "978-3-540-71602-0",
  DOI =          "doi:10.1007/978-3-540-71605-1_1",
  abstract =     "The ability of Genetic Programming to scale to
                 problems of increasing difficulty operates on the
                 premise that it is possible to capture regularities
                 that exist in a problem environment by decomposition of
                 the problem into a hierarchy of modules. As computer
                 scientists and more generally as humans we tend to
                 adopt a similar divide-and-conquer strategy in our
                 problem solving. In this paper we consider the adoption
                 of such a strategy for Genetic Algorithms. By adopting
                 a modular representation in a Genetic Algorithm we can
                 make efficiency gains that enable superior scaling
                 characteristics to problems of increasing size. We
                 present a comparison of two modular Genetic Algorithms,
                 one of which is a Grammatical Genetic Programming
                 algorithm, the meta-Grammar Genetic Algorithm (mGGA),
                 which generates binary string sentences instead of
                 traditional GP trees. A number of problems instances
                 are tackled which extend the Checkerboard problem by
                 introducing different kinds of regularity and noise.
                 The results demonstrate some limitations of the modular
                 GA (MGA) representation and how the mGGA can overcome
                 these. The mGGA shows improved scaling when compared
                 the MGA.",
  notes =        "Part of \cite{ebner:2007:GP} EuroGP'2007 held in
                 conjunction with EvoCOP2007, EvoBIO2007 and

Genetic Programming entries for Erik Hemberg Conor Gilligan Michael O'Neill Anthony Brabazon