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Parameter Tuning of MOEAs Using a Bilevel Optimization Approach
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: 0000-0001-5436-2128
University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. (Produktion och automatiseringsteknik, Production and Automation Engineering)ORCID iD: 0000-0003-0111-1776
University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. (Produktion och automatiseringsteknik, Production and Automation Engineering)ORCID iD: 0000-0003-3973-3394
2015 (English)In: Evolutionary Multi-Criterion Optimization: 8th International Conference, EMO 2015, Guimarães, Portugal, March 29 --April 1, 2015. Proceedings, Part I / [ed] António Gaspar-Cunha, Carlos Henggeler Antunes & Carlos Coello Coello, Springer, 2015, 233-247 p.Conference paper, (Refereed)
Abstract [en]

The performance of an Evolutionary Algorithm (EA) can be greatly influenced by its parameters. The optimal parameter settings are also not necessarily the same across different problems. Finding the optimal set of parameters is therefore a difficult and often time-consuming task. This paper presents results of parameter tuning experiments on the NSGA-II and NSGA-III algorithms using the ZDT test problems. The aim is to gain new insights on the characteristics of the optimal parameter settings and to study if the parameters impose the same effect on both NSGA-II and NSGA-III. The experiments also aim at testing if the rule of thumb that the mutation probability should be set to one divided by the number of decision variables is a good heuristic on the ZDT problems. A comparison of the performance of NSGA-II and NSGA-III on the ZDT problems is also made.

Place, publisher, year, edition, pages
Springer, 2015. 233-247 p.
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 9018
Keyword [en]
Parameter tuning, NSGA-II, NSGA-III, ZDT, Bilevel optimization, Multi-objective problems
National Category
Computer and Information Science
Research subject
Technology
Identifiers
URN: urn:nbn:se:his:diva-11371DOI: 10.1007/978-3-319-15934-8_16Scopus ID: 2-s2.0-84925342559ISBN: 978-3-319-15933-1 ISBN: 978-3-319-15934-8 OAI: oai:DiVA.org:his-11371DiVA: diva2:846920
Conference
8th International Conference on Evolutionary Multi-Criterion Optimization, 29 March-1 April 2015, Guimarães, Portugal
Available from: 2015-08-18 Created: 2015-08-18 Last updated: 2015-12-18Bibliographically approved

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CiteExportLink to record
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Citation style
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