Optimizing Shape Design with Distributed Parallel Genetic Programming on GPUs

Created by W.Langdon from gp-bibliography.bib Revision:1.4496

  author =       "Simon Harding and W. Banzhaf",
  title =        "Optimizing Shape Design with Distributed Parallel
                 Genetic Programming on GPUs",
  booktitle =    "Parallel Architectures and Bioinspired Algorithms",
  publisher =    "Springer",
  year =         "2012",
  editor =       "Francisco {Fernandez de Vega} and 
                 Jose Ignacio {Hidalgo Perez} and Juan Lanchares",
  volume =       "415",
  series =       "Studies in Computational Intelligence",
  chapter =      "2",
  pages =        "51--75",
  keywords =     "genetic algorithms, genetic programming, Cartesian
                 Genetic Programming, GPU",
  isbn13 =       "978-3-642-28788-6",
  URL =          "http://www.amazon.com/Architectures-Bioinspired-Algorithms-Computational-Intelligence/dp/3642287883",
  DOI =          "doi:10.1007/978-3-642-28789-3_3",
  abstract =     "Optimised shape design is used for such applications
                 as wing design in aircraft, hull design in ships, and
                 more generally rotor optimisation in turbomachinery
                 such as that of aircraft, ships, and wind turbines. We
                 present work on optimized shape design using a
                 technique from the area of Genetic Programming,
                 self-modifying Cartesian Genetic Programming (SMCGP),
                 to evolve shapes with specific criteria, such as
                 minimised drag or maximised lift. This technique is
                 well suited for a distributed parallel system to
                 increase efficiency. Fitness evaluation of the genetic
                 programming technique is accomplished through a custom
                 implementation of a fluid dynamics solver running on
                 graphics processing units (GPUs). Solving fluid
                 dynamics systems is a computationally expensive task
                 and requires optimisation in order for the evolution to
                 complete in a practical period of time. In this
                 chapter, we shall describe both the SMCGP technique and
                 the GPU fluid dynamics solver that together provide a
                 robust and efficient shape design system.",
  affiliation =  "IDSIA (Istituto Dalle Molle di Studi sull'Intelligenza
                 Artificiale), Lugano, Switzerland",

Genetic Programming entries for Simon Harding Wolfgang Banzhaf