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Metamodel-based prediction of performance metrics for bilevel parameter tuning in MOEAs
University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. (Produktion och automatiseringsteknik, Production and Automation Engineering)ORCID iD: 0000-0001-5436-2128
University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. (Produktion och automatiseringsteknik, Production and Automation Engineering)
University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. (Produktion och automatiseringsteknik, Production and Automation Engineering)ORCID iD: 0000-0003-0111-1776
2016 (English)In: 2016 IEEE Congress on Evolutionary Computation (CEC), New York: IEEE, 2016, 1909-1916 p.Conference paper, (Refereed)
Abstract [en]

We consider a bilevel parameter tuning problem where the goal is to maximize the performance of a given multi-objective evolutionary optimizer on a given problem. The search for optimal algorithmic parameters requires the assessment of several sets of parameters, through multiple optimization runs, in order to mitigate the effect of noise that is inherent to evolutionary algorithms. This task is computationally expensive and therefore, in this paper, we propose to use sampling and metamodeling to approximate the performance of the optimizer as a function of its parameters. While such an approach is not unheard of, the choice of the metamodel to be used still remains unclear. The aim of this paper is to empirically compare 11 different metamodeling techniques with respect to their accuracy and training times in predicting two popular multi-objective performance metrics, namely, the hypervolume and the inverted generational distance. For the experiments in this pilot study, NSGA-II is used as the multi-objective optimizer for solving ZDT problems, 1 through 4.

Place, publisher, year, edition, pages
New York: IEEE, 2016. 1909-1916 p.
Keyword [en]
Parameter tuning, Evolutionary computation, Metamodeling, Bilevel optimization
National Category
Computer Science
Research subject
Technology
Identifiers
URN: urn:nbn:se:his:diva-13285DOI: 10.1109/CEC.2016.7744021ISI: 000390749102012Scopus ID: 2-s2.0-85008256466ISBN: 978-1-5090-0623-6 (electronic)ISBN: 978-1-5090-0622-9 (print)ISBN: 978-1-5090-0624-3 (print)OAI: oai:DiVA.org:his-13285DiVA: diva2:1061508
Conference
2016 IEEE Congress on Evolutionary Computation, CEC 2016, Vancouver, Canada, July 24-29, 2016
Available from: 2017-01-02 Created: 2017-01-02 Last updated: 2017-05-23Bibliographically approved

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
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  • asciidoc
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