Duro, J.A. and Saxena, D.K. (2017) Timing the decision support for real-world many-objective optimization problems. In: Trautmann, H., Rudolph, G., Klamroth, K., Schütze, O., Wiecek, M., Jin, Y. and Grimme, C., (eds.) Evolutionary Multi-Criterion Optimization : 9th International Conference, EMO 2017, Proceedings. 9th International Conference on Evolutionary Multi-Criterion Optimization, 19-22 Mar 2017, Münster, Germany. Lecture Notes in Computer Science (10173). Springer International Publishing , pp. 191-205. ISBN 9783319541563
Abstract
Lately, there is growing emphasis on improving the scalability of multi-objective evolutionary algorithms (MOEAs) so that many-objective problems (characterized by more than three objectives) can be effectively dealt with. Alternatively, the utility of integrating decision maker’s (DM’s) preferences into the optimization process so as to target some most preferred solutions by the DM (instead of the whole Pareto-optimal front), is also being increasingly recognized. The authors here, have earlier argued that despite the promises in the latter approach, its practical utility may be impaired by the lack of—objectivity, repeatability, consistency, and coherence in the DM’s preferences. To counter this, the authors have also earlier proposed a machine learning based decision support framework to reveal the preference-structure of objectives. Notably, the revealed preference-structure may be sensitive to the timing of application of this framework along an MOEA run. In this paper the authors counter this limitation, by integrating a termination criterion with an MOEA run, towards determining the appropriate timing for application of the machine learning based framework. Results based on three real-world many-objective problems considered in this paper, highlight the utility of the proposed integration towards an objective, repeatable, consistent, and coherent decision support for many-objective problems.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Editors: |
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Copyright, Publisher and Additional Information: | © 2017 Springer International Publishing AG. This is an author-produced version of a paper subsequently published in EMO 2017 Proceedings. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Decision Support; Dissimilarity Measure; Relative Percentage Difference; Correlation Strength; Multiple Criterion Decision Making |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 29 Oct 2020 08:51 |
Last Modified: | 30 Oct 2020 10:26 |
Status: | Published |
Publisher: | Springer International Publishing |
Series Name: | Lecture Notes in Computer Science |
Refereed: | Yes |
Identification Number: | 10.1007/978-3-319-54157-0_14 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:167301 |