Evolution of Covariance Functions for Gaussian Process Regression using Genetic Programming

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

@InProceedings{Kronberger:2013:EuroCAST,
  title =        "Evolution of Covariance Functions for Gaussian Process
                 Regression using Genetic Programming",
  author =       "Gabriel Kronberger and Michael Kommenda",
  howpublished = "arXiv",
  booktitle =    "EuroCAST 2013",
  year =         "2013",
  series =       "LNCS",
  address =      "Las Palmas, Canary Islands, Spain",
  month =        "10-15 " # feb,
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming",
  bibdate =      "2013-06-02",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/db/journals/corr/corr1305.html#abs-1305-3794",
  URL =          "http://arxiv.org/abs/1305.3794",
  size =         "8 pages",
  abstract =     "In this contribution we describe an approach to evolve
                 composite covariance functions for Gaussian processes
                 using genetic programming. A critical aspect of
                 Gaussian processes and similar kernel-based models such
                 as SVM is, that the covariance function should be
                 adapted to the modelled data. Frequently, the squared
                 exponential covariance function is used as a default.
                 However, this can lead to a misspecified model, which
                 does not fit the data well.

                 In the proposed approach we use a grammar for the
                 composition of covariance functions and genetic
                 programming to search over the space of sentences that
                 can be derived from the grammar.

                 We tested the proposed approach on synthetic data from
                 two-dimensional test functions, and on the Mauna Loa
                 carbon dioxide time series. The results show, that our
                 approach is feasible, finding covariance functions that
                 perform much better than a default covariance function.
                 For the CO2 data set a composite covariance function is
                 found, that matches the performance of a hand-tuned
                 covariance function.",
  notes =        "EuroCAST 2013 not yet published (Aug 2013),

                 arXiv 22 May 2013 Also known as
                 \cite{journals/corr/abs-1305-3794}

                 Presented at the Workshop Theory and Applications of
                 Metaheuristic Algorithms, EUROCAST2013. To appear in
                 selected papers of Computer Aided Systems Theory -
                 EUROCAST 2013; Volumes Editors: Roberto Moreno-Diaz,
                 Franz R. Pichler, Alexis Quesada-Arencibia; LNCS
                 Springer",
}

Genetic Programming entries for Gabriel Kronberger Michael Kommenda

Citations