Analyzing Module Usage in Grammatical Evolution

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{conf/ppsn/SwaffordHOB12,
  author =       "John Mark Swafford and Erik Hemberg and 
                 Michael O'Neill and Anthony Brabazon",
  title =        "Analyzing Module Usage in Grammatical Evolution",
  booktitle =    "Parallel Problem Solving from Nature, PPSN XII (part
                 1)",
  year =         "2012",
  editor =       "Carlos A. {Coello Coello} and Vincenzo Cutello and 
                 Kalyanmoy Deb and Stephanie Forrest and 
                 Giuseppe Nicosia and Mario Pavone",
  volume =       "7491",
  series =       "Lecture Notes in Computer Science",
  pages =        "347--356",
  address =      "Taormina, Italy",
  month =        sep # " 1-5",
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming, grammatical
                 evolution",
  isbn13 =       "978-3-642-32936-4",
  DOI =          "doi:10.1007/978-3-642-32937-1_35",
  size =         "10 pages",
  abstract =     "Being able to exploit modularity in genetic
                 programming (GP) is an open issue and a promising vein
                 of research. Previous work has identified a variety of
                 methods of finding and using modules, but little is
                 reported on how the modules are being used in order to
                 yield the observed performance gains. In this work,
                 multiple methods for identifying modules are applied to
                 some common, dynamic benchmark problems. Results show
                 there is little difference in the performance of the
                 approaches. However, trends in how modules are used and
                 how good individuals use these modules are seen. These
                 trends indicate that discovered modules can be used
                 frequently and by good individuals. Further examination
                 of the modules uncovers that useful as well as
                 unhelpful modules are discovered and used frequently.
                 The results suggest directions for future work in
                 improving module manipulation via crossover and
                 mutation and module usage in the population.",
  bibsource =    "DBLP, http://dblp.uni-trier.de",
  affiliation =  "Natural Computing Research & Applications Group,
                 Complex and Adaptive Systems Laboratory, University
                 College Dublin, Ireland",
}

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

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