Symbolic Regression of Discontinuous and Multivariate Functions by Hyper-Volume Error Separation (HVES)

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{Fillon:2007:cec,
  author =       "Cyril Fillon and Alberto Bartoli",
  title =        "Symbolic Regression of Discontinuous and Multivariate
                 Functions by Hyper-Volume Error Separation (HVES)",
  booktitle =    "2007 IEEE Congress on Evolutionary Computation",
  year =         "2007",
  editor =       "Dipti Srinivasan and Lipo Wang",
  pages =        "23--30",
  address =      "Singapore",
  month =        "25-28 " # sep,
  organization = "IEEE Computational Intelligence Society",
  publisher =    "IEEE Press",
  ISBN =         "1-4244-1340-0",
  file =         "1757.pdf",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1109/CEC.2007.4424450",
  abstract =     "Symbolic regression is aimed at discovering
                 mathematical expressions, in symbolic form, that fit a
                 given sample of data points. While Genetic Programming
                 (GP) constitutes a powerful tool for solving this class
                 of problems, its effectiveness is still severely
                 limited when the data sample requires different
                 expressions in different regions of the input space -
                 i.e., when the approximating function should be
                 discontinuous. In this paper we present a new GP-based
                 approach for symbolic regression of discontinuous
                 functions in multivariate data-sets. We identify the
                 portions of the input space that require different
                 approximating functions by means of a new algorithm
                 that we call Hyper-Volume Error Separation (HVES). To
                 this end we run a preliminary GP evolution and
                 partition the input space based on the error exhibited
                 by the best individual across the data-set. Then we
                 partition the data-set based on the partition of the
                 input space and use each such partition for driving an
                 independent, preliminary GP evolution. The populations
                 resulting from such preliminary evolutions are finally
                 merged and evolved again. We compared our approach to
                 the standard GP search and to a GP search for
                 discontinuous functions in univariate data-sets. Our
                 results show that coupling HVES with GP is an effective
                 approach and provides significant accuracy improvements
                 while requiring less computational resources.",
  notes =        "CEC 2007 - A joint meeting of the IEEE, the EPS, and
                 the IET.

                 IEEE Catalog Number: 07TH8963C",
}

Genetic Programming entries for Cyril Fillon Alberto Bartoli

Citations