N-Dimensional Surface Mapping Using Genetic Programming

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

@InProceedings{corney:1999:NSMUGP,
  author =       "David Corney and Ian Parmee",
  title =        "N-Dimensional Surface Mapping Using Genetic
                 Programming",
  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
                 papers",
  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
                 (GP-99)

                 MSc Thesis -- text from
                 http://www.cs.ucl.ac.uk/staff/D.Corney/MSc_thesis_abstract.html

                 N-Dimensional Surface Mapping Using Genetic
                 Programming

                 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

Citations