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Desirable properties of performance indicators for assessing interactive evolutionary multiobjective optimization methods
University of Jyväskylä, Faculty of Information Technology, Finland.
University of Skövde, School of Engineering Science. University of Skövde, Virtual Engineering Research Environment. (Produktion och Automatiseringsteknik, Production and Automation Engineering)ORCID iD: 0000-0001-5436-2128
University of Jyväskylä, Faculty of Information Technology, Finland.
University of Jyväskylä, Faculty of Information Technology, Finland.
2022 (English)In: GECCO '22: Proceedings of the 2022 Genetic and Evolutionary Computation Conference / [ed] Jonathan E. Fieldsend; Markus Wagner, Association for Computing Machinery (ACM), 2022, p. 1803-1811Conference paper, Published paper (Refereed)
Abstract [en]

Interactive methods support decision makers in finding the most preferred solution in multiobjective optimization problems. They iteratively incorporate the decision maker's preference information to find the best balance among conflicting objectives. Several interactive methods have been developed in the literature. However, choosing the most suitable interactive method for a given problem can prove challenging and appropriate indicators are needed to compare interactive methods. Some indicators exist for a priori methods, where preferences are provided at the beginning of the solution process. We present some numerical experiments that illustrate why these indicators are not suitable for interactive methods. As the main contribution of this paper, we propose a set of desirable properties of indicators for assessing interactive methods as the first step of filling a gap in the literature. We discuss each property in detail and provide simple examples to illustrate their behavior. 

Place, publisher, year, edition, pages
Association for Computing Machinery (ACM), 2022. p. 1803-1811
Keywords [en]
Decision making, Multiobjective optimization, Numerical methods, Decision makers, Evolutionary multiobjective optimization, Interactive methods, Multiple-criterion optimization, Optimization method, Performance assessment, Performance indicators, Performances evaluation, Preferred solutions, Property, Iterative methods, multiple criteria optimization, performance evaluation
National Category
Computer Sciences Information Systems Interaction Technologies
Research subject
Production and Automation Engineering; VF-KDO
Identifiers
URN: urn:nbn:se:his:diva-21743DOI: 10.1145/3520304.3533955ISI: 001035469400282Scopus ID: 2-s2.0-85136330864ISBN: 978-1-4503-9268-6 (print)OAI: oai:DiVA.org:his-21743DiVA, id: diva2:1692128
Conference
2022 Genetic and Evolutionary Computation Conference, GECCO '22, July 9-13, 2022, Boston, Massachusetts
Part of project
Virtual factories with knowledge-driven optimization (VF-KDO), Knowledge Foundation
Note

© 2022 ACM

Available from: 2022-09-01 Created: 2022-09-01 Last updated: 2024-06-19Bibliographically approved

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Bandaru, Sunith

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