Evolving Intervening Variables for Response Surface Approximations

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

@InProceedings{Sobester:2004:AIAA,
  author =       "Andras Sobester and Prasanth B. Nair and 
                 Andy J. Keane",
  title =        "Evolving Intervening Variables for Response Surface
                 Approximations",
  booktitle =    "10th AIAA/ISSMO Multidisciplinary Analysis and
                 Optimization Conference",
  year =         "2004",
  number =       "AIAA 2004-4379",
  pages =        "1--12",
  month =        "30-" # aug # " 1-" # sep,
  series =       "{}",
  address =      "Albany, New York, USA",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.soton.ac.uk/~as7/publ/mao04.pdf",
  size =         "12 pages",
  abstract =     "Genetic Programming (GP) is a powerful string
                 processing technique based on the Darwinian paradigm of
                 natural selection. Although initially conceived with
                 the more general aim of automatically producing
                 computer code for complex tasks, it can also be used to
                 evolve symbolic expressions, provided that we have a
                 fitness criterion that measures the quality of an
                 expression. In this paper we present a GP approach for
                 generating functions in closed analytic form that map
                 the input space of a complex function approximation
                 problem into one where the output is more amenable to
                 linear regression. In other words, intervening
                 variables are evolved in each dimension, such that the
                 final approximation model has good generalization
                 properties and at the same time, due to its linearity,
                 can easily be incorporated into further calculations.
                 We employ least squares and cross-validation error
                 measures to derive the fitness function that drives the
                 evolutionary process. Results are presented for a
                 one-dimensional test problem to illustrate some of the
                 proposed ideas - this is followed by a more thorough
                 empirical study, including multi-dimensional
                 approximations and an engineering design problem.",
}

Genetic Programming entries for Andras Sobester Prasanth B Nair Andy J Keane

Citations