Automatic innovative truss design using grammatical evolution

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

@Article{Fenton:2014:AC,
  author =       "Michael Fenton and Ciaran McNally and 
                 Jonathan Byrne and Erik Hemberg and James McDermott and 
                 Michael O'Neill",
  title =        "Automatic innovative truss design using grammatical
                 evolution",
  journal =      "Automation in Construction",
  year =         "2014",
  volume =       "39",
  pages =        "59--69",
  note =         "Bronze Humie winner",
  keywords =     "genetic algorithms, genetic programming, Grammatical
                 evolution, Structural optimisation, Evolutionary
                 computation, Truss design, Computer aided design",
  ISSN =         "0926-5805",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0926580513002124",
  URL =          "http://www.human-competitive.org/sites/default/files/fenton-paper-from-2014-for-background.pdf",
  URL =          "http://www.human-competitive.org/sites/default/files/fenton-text.txt",
  DOI =          "doi:10.1016/j.autcon.2013.11.009",
  size =         "11 pages",
  abstract =     "Truss optimization in the field of Structural
                 Engineering is a growing discipline. The application of
                 Grammatical Evolution, a grammar-based form of Genetic
                 Programming (GP), has shown that it is capable of
                 generating innovative engineering designs. Existing
                 truss optimization methods in GP focus primarily on
                 optimizing global topology. The standard method is to
                 explore the search space while seeking minimum
                 cross-sectional areas for all elements. In doing so,
                 critical knowledge of section geometry and orientation
                 is omitted, leading to inaccurate stress calculations
                 and structures not meeting codes of practice. This can
                 be addressed by constraining the optimisation method to
                 only use standard construction elements.

                 The aim of this paper is not to find fully optimized
                 solutions, but rather to show that solutions very close
                 to the theoretical optimum can be achieved using
                 real-world elements. This methodology can be applied to
                 any structural engineering design which can be
                 generated by a grammar.",
  notes =        "Humies http://www.human-competitive.org/awards
                 https://pbs.twimg.com/media/DFHp6iTXkAQO613.jpg",
}

Genetic Programming entries for Michael Fenton Ciaran McNally Jonathan Byrne Erik Hemberg James McDermott Michael O'Neill

Citations