- 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}",
