Evolution of Covariance Functions for Gaussian Process Regression using Genetic Programming

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7 Citations (Scopus)

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 modeled 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 CO 2 time series. The results show, that our approach is feasible, finding covariance functions that perform much better than a default covariance function. For the CO 2 data set a composite covariance function is found, that matches the performance of a hand-tuned covariance function.

Original languageEnglish
Title of host publicationComputer Aided Systems Theory, EUROCAST 2013 - 14th International Conference, Revised Selected Papers
PublisherSpringer
Pages308-315
Number of pages8
EditionPART 1
ISBN (Print)978-3-642-53855-1
DOIs
Publication statusPublished - 2013
Event14th International Conference on Computer Aided Systems Theory, Eurocast 2013 - Las Palmas de Gran Canaria, Spain
Duration: 10 Feb 201315 Feb 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume8111 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference14th International Conference on Computer Aided Systems Theory, Eurocast 2013
CountrySpain
CityLas Palmas de Gran Canaria
Period10.02.201315.02.2013

Keywords

  • Gaussian Process
  • Genetic Programming
  • Structure Identification

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