Evolutionary Design using Grammatical Evolution and Shape Grammars: Designing a Shelter

Created by W.Langdon from gp-bibliography.bib Revision:1.2031

@Article{oneill_etal:ijde2010,
  author =       "Michael O'Neill and James McDermott and 
                 John Mark Swafford and Jonathan Byrne and Erik Hemberg and 
                 Anthony Brabazon and Elizabeth Shotton and 
                 Ciaran McNally and Martin Hemberg",
  title =        "Evolutionary Design using Grammatical Evolution and
                 Shape Grammars: Designing a Shelter",
  journal =      "International Journal of Design Engineering",
  year =         "2010",
  volume =       "3",
  number =       "1",
  pages =        "4--24",
  keywords =     "genetic algorithms, genetic programming, grammatical
                 evolution, evolutionary design, architecture, shape
                 grammars, evolutionary computation, shelter design,
                 design engineering",
  URL =          "http://inderscience.metapress.com/openurl.asp?genre=article&issn=1751-5874&volume=3&issue=1&spage=4",
  doi =          "doi:10.1504/IJDE.2010.032820",
  abstract =     "A new evolutionary design tool is presented, which
                 uses shape grammars and a grammar-based form of
                 evolutionary computation, grammatical evolution (GE).
                 Shape grammars allow the user to specify possible
                 forms, and GE allows forms to be iteratively selected,
                 recombined and mutated: this is shown to be a powerful
                 combination of techniques. The potential of GE and
                 shape grammars for evolutionary design is examined by
                 attempting to design a single-person shelter to be
                 evaluated by collaborators from the University College
                 Dublin School of Architecture, Landscape, and
                 Engineering. The team was able to successfully generate
                 conceptual shelter designs based on scrutiny from the
                 collaborators. A number of avenues for future work are
                 highlighted arising from the case study.",
}

Genetic Programming entries for Michael O'Neill James McDermott John Mark Swafford Jonathan Byrne Erik Hemberg Anthony Brabazon Elizabeth Shotton Ciaran McNally Martin Hemberg