Evolving spring-mass models: a test-bed for graph encoding schemes

Created by W.Langdon from gp-bibliography.bib Revision:1.4340

  author =       "Simon Lucas",
  title =        "Evolving spring-mass models: a test-bed for graph
                 encoding schemes",
  booktitle =    "Proceedings of the 2002 Congress on Evolutionary
                 Computation CEC2002",
  editor =       "David B. Fogel and Mohamed A. El-Sharkawi and 
                 Xin Yao and Garry Greenwood and Hitoshi Iba and Paul Marrow and 
                 Mark Shackleton",
  pages =        "1952--1957",
  year =         "2002",
  publisher =    "IEEE Press",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  organisation = "IEEE Neural Network Council (NNC), Institution of
                 Electrical Engineers (IEE), Evolutionary Programming
                 Society (EPS)",
  ISBN =         "0-7803-7278-6",
  month =        "12-17 " # may,
  notes =        "CEC 2002 - A joint meeting of the IEEE, the
                 Evolutionary Programming Society, and the IEE. Held in
                 connection with the World Congress on Computational
                 Intelligence (WCCI 2002)",
  URL =          "http://algoval.essex.ac.uk/rep/springs/cec2002.pdf",
  DOI =          "doi:10.1109/CEC.2002.1004542",
  keywords =     "genetic algorithms, genetic programming, evolving
                 spring-mass models, graph encoding schemes, height
                 challenge design problem, performance evaluation,
                 planar graph coding scheme, CAD, computational
                 geometry, evolutionary computation, graph theory",
  abstract =     "For many interesting design problems the solution is
                 most naturally represented as a type of graph. This
                 paper proposes that the problem of evolving spring-mass
                 models for a set of design challenges makes an
                 excellent test-bed for evaluating the performance of
                 various graph encoding schemes. We describe how the
                 problem is set up, and intro-duce a planar graph coding
                 scheme. Results demonstrate that the planar graph
                 encoding scheme significantly out-performs a simple
                 direct encoding scheme on a height-challenge design

Genetic Programming entries for Simon M Lucas