N-Dimensional Surface Mapping Using Genetic Programming

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

  author =       "David Corney and Ian Parmee",
  title =        "N-Dimensional Surface Mapping Using Genetic
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "1999",
  editor =       "Wolfgang Banzhaf and Jason Daida and 
                 Agoston E. Eiben and Max H. Garzon and Vasant Honavar and 
                 Mark Jakiela and Robert E. Smith",
  volume =       "2",
  pages =        "1230",
  address =      "Orlando, Florida, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "13-17 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming, poster
  ISBN =         "1-55860-611-4",
  URL =          "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GP-424.pdf",
  URL =          "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GP-424.ps",
  notes =        "GECCO-99 A joint meeting of the eighth international
                 conference on genetic algorithms (ICGA-99) and the
                 fourth annual genetic programming conference

                 MSc Thesis -- text from

                 N-Dimensional Surface Mapping Using Genetic

                 This work introduces an extension to Genetic
                 Programming (GP) known as {"}GP-UDF{"} which uses
                 multiple User-Defined Functions (UDFs) to solve
                 surface-mapping problems. These UDFs are high-level
                 primitives, such as hills and polynomials, which
                 compress the information required to map a surface.
                 UDFs can be used to add real-world knowledge to a
                 genetic search, and also to analyse and classify
                 high-dimensional surfaces. GP-UDF also produces more
                 readable solutions than standard GP.

                 The results show that, for the problems considered,
                 GP-UDF does not produce more accurate models than
                 standard GP. However, the results also suggest that
                 GP-UDF could be used as a {"}landscape classifier{"}, a
                 tool for analysing high-dimensional surfaces to
                 identify characteristic features.

                 An important consideration in systems identification is
                 the transparency (i.e. readability), of a model. GP-UDF
                 is compared with neural networks (both MLP and RBF
                 networks), and is shown to be far more readable, with
                 the cost of being less accurate.",

Genetic Programming entries for David Corney Ian C Parmee