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A parameterless performance metric for reference-point based multi-objective evolutionary algorithms
University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. (Simulation-Based Optimization)ORCID iD: 0000-0001-5436-2128
University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. (Simulation-Based Optimization)ORCID iD: 0000-0003-3124-0077
2019 (English)In: GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference / [ed] Manuel López-Ibáñez, New York, NY, USA: ACM Digital Library, 2019, p. 499-506Conference paper, Published paper (Refereed)
Abstract [en]

Most preference-based multi-objective evolutionary algorithms use reference points to articulate the decision maker's preferences. Since these algorithms typically converge to a sub-region of the Pareto-optimal front, the use of conventional performance measures (such as hypervolume and inverted generational distance) may lead to misleading results. Therefore, experimental studies in preference-based optimization often resort to using graphical methods to compare various algorithms. Though a few ad-hoc measures have been proposed in the literature, they either fail to generalize or involve parameters that are non-intuitive for a decision maker. In this paper, we propose a performance metric that is simple to implement, inexpensive to compute, and most importantly, does not involve any parameters. The so called expanding hypercube metric has been designed to extend the concepts of convergence and diversity to preference optimization. We demonstrate its effectiveness through constructed preference solution sets in two and three objectives. The proposed metric is then used to compare two popular reference-point based evolutionary algorithms on benchmark optimization problems up to 20 objectives.

Place, publisher, year, edition, pages
New York, NY, USA: ACM Digital Library, 2019. p. 499-506
Keywords [en]
multi-objective optimization, decision making, reference point, performance metric, comparison
National Category
Computer Sciences
Research subject
VF-KDO; Production and Automation Engineering
Identifiers
URN: urn:nbn:se:his:diva-17515DOI: 10.1145/3321707.3321757ISBN: 978-1-4503-6111-8 (electronic)OAI: oai:DiVA.org:his-17515DiVA, id: diva2:1341872
Conference
Genetic and Evolutionary Computation Conference, GECCO 2019, Prague, Czech Republic, July 13-17, 2019
Funder
Knowledge Foundation, 41459Available from: 2019-08-12 Created: 2019-08-12 Last updated: 2019-10-08Bibliographically approved

Open Access in DiVA

The full text will be freely available from 2020-08-01 00:00
Available from 2020-08-01 00:00

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Bandaru, SunithSmedberg, Henrik

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
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More styles
Language
  • de-DE
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  • Other locale
More languages
Output format
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  • asciidoc
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