Discrete Planar Truss Optimization by Node Position Variation using Grammatical Evolution

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@Article{Fenton:2016:ieeeTEC,
  author =       "Michael Fenton and Ciaran McNally and 
                 Jonathan Byrne and Erik Hemberg and James McDermott and 
                 Michael O'Neill",
  title =        "Discrete Planar Truss Optimization by Node Position
                 Variation using Grammatical Evolution",
  journal =      "IEEE Transactions on Evolutionary Computation",
  year =         "2016",
  volume =       "20",
  number =       "4",
  pages =        "577--589",
  month =        aug,
  keywords =     "genetic algorithms, genetic programming, Grammatical
                 Evolution, Civil Engineering, Computational
                 Intelligence, Evolutionary Computation, Structural
                 Engineering",
  ISSN =         "1089-778X",
  URL =          "http://www.human-competitive.org/sites/default/files/fenton-paper.pdf",
  URL =          "http://www.human-competitive.org/sites/default/files/fenton-text.txt",
  DOI =          "doi:10.1109/TEVC.2015.2502841",
  size =         "13 pages",
  abstract =     "The majority of existing discrete truss optimization
                 methods focus primarily on optimizing global truss
                 topology using a ground structure approach, in which
                 all possible node and beam locations are specified a
                 priori. The ground structure discrete optimization
                 method has been shown to be restrictive as it limits
                 derivable solutions to what is explicitly defined.
                 Greater representational freedom can improve
                 performance. In this paper Grammatical Evolution is
                 applied. It can represent a variable number of nodes
                 and their locations on a continuum. A novel method of
                 connecting evolved nodes using a Delaunay triangulation
                 algorithm shows that fully triangulated, kinematically
                 stable structures can be generated. Discrete beamtruss
                 structures can be optimized without the need for any
                 information about the desired form of the solution
                 other than the design envelope. Our technique is
                 compared to existing discrete optimization techniques,
                 and notable savings in structure self weight are
                 demonstrated. In particular our new method can produce
                 results superior to those reported in the literature in
                 cases where the problem is ill-defined and the
                 structure of the solution is not known a priori.",
  notes =        "Entered 2017 Humies
                 http://www.human-competitive.org/awards,

                 Also known as \cite{7335624}",
}

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

Citations