Evolution of a Strategy for Ship Guidance Using Two Implementations of Genetic Programming

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

@InProceedings{eurogp:Alfaro-CidMM05,
  author =       "Eva Alfaro-Cid and Euan William McGookin and 
                 David James Murray-Smith",
  editor =       "Maarten Keijzer and Andrea Tettamanzi and 
                 Pierre Collet and Jano I. {van Hemert} and Marco Tomassini",
  title =        "Evolution of a Strategy for Ship Guidance Using Two
                 Implementations of Genetic Programming",
  booktitle =    "Proceedings of the 8th European Conference on Genetic
                 Programming",
  publisher =    "Springer",
  series =       "Lecture Notes in Computer Science",
  volume =       "3447",
  year =         "2005",
  address =      "Lausanne, Switzerland",
  month =        "30 " # mar # " - 1 " # apr,
  organisation = "EvoNet",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-25436-6",
  pages =        "250--260",
  DOI =          "doi:10.1007/b107383",
  bibsource =    "DBLP, http://dblp.uni-trier.de",
  abstract =     "In this paper the implementation of Genetic
                 Programming (GP) to optimise a controller structure for
                 a supply ship is assessed. GP is used to evolve control
                 strategies that, given the current and desired state of
                 the propulsion and heading dynamics of a supply ship as
                 inputs, generate the commanded forces required to
                 manoeuvre the ship. The optimised controllers are
                 evaluated through computer simulations and real
                 manoeuvrability tests in a water basin laboratory. In
                 order to deal with the issue of the generation of
                 numerical constants, two kinds of GP algorithms are
                 implemented. The first one chooses the constants
                 necessary to create the controller structure by random
                 generation . The second algorithm includes a Genetic
                 Algorithms (GAs) technique for the optimisation of such
                 constants. The results obtained illustrate the benefits
                 of using GP to optimise propulsion and navigation
                 controllers for ships.",
  notes =        "Part of \cite{keijzer:2005:GP} EuroGP'2005 held in
                 conjunction with EvoCOP2005 and EvoWorkshops2005",
}

Genetic Programming entries for Eva Alfaro-Cid Euan William McGookin David James Murray-Smith

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