Preferences-based choice prediction in evolutionary multi-objective optimization
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
Evolutionary multi-objective algorithms (EMOAs) of the type of NSGA-2 approximate the Pareto-front, after which a decisionmaker (DM) is confounded with the primary task of selecting the best solution amongst all the equally good solutions on the Pareto-front. In this paper, we complement the popular NSGA-2 EMOA by posteriori identifying a DM’s best solution among the candidate solutions on the Pareto-front, generated through NSGA-2. To this end, we employ a preference-based learning approach to learn an abstract ideal reference point of the DM on the multi-objective space, which reflects the compromises the DM makes against a set of conflicting objectives. The solution that is closest to this reference-point is then predicted as the DM’s best solution. The pairwise comparisons of the candidate solutions provides the training information for our learning model. The experimental results on ZDT1 dataset shows that the proposed approach is not only intuitive, but also easy to apply, and robust to inconsistencies in the DM’s preference statements.
Original language | English |
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Title of host publication | Applications of Evolutionary Computation - 20th European Conference, EvoApplications 2017, Proceedings |
Editors | J.Ignacio Hidalgo, Carlos Cotta, Ting Hu, Alberto Tonda, Paolo Burrelli, Matt Coler, Giovanni Iacca, Michael Kampouridis, Antonio M. Mora Garcia, Giovanni Squillero, Anthony Brabazon, Evert Haasdijk, Jacqueline Heinerman, Fabio D Andreagiovanni, Jaume Bacardit, Trung Thanh Nguyen, Sara Silva, Ernesto Tarantino, Anna I. Esparcia-Alcazar, Gerd Ascheid, Kyrre Glette, Stefano Cagnoni, Paul Kaufmann, Francisco Fernandez de Vega, Michalis Mavrovouniotis, Mengjie Zhang, Federico Divina, Kevin Sim, Neil Urquhart, Robert Schaefer |
Number of pages | 10 |
Publisher | Springer Verlag, |
Publication date | 1 Jan 2017 |
Pages | 715-724 |
ISBN (Print) | 9783319558486 |
DOIs | |
Publication status | Published - 1 Jan 2017 |
Externally published | Yes |
Event | 20th European Conference on the Applications of Evolutionary Computation, EvoApplications 2017 - Amsterdam, Netherlands Duration: 19 Apr 2017 → 21 Apr 2017 |
Conference
Conference | 20th European Conference on the Applications of Evolutionary Computation, EvoApplications 2017 |
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Land | Netherlands |
By | Amsterdam |
Periode | 19/04/2017 → 21/04/2017 |
Series | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 10199 LNCS |
ISSN | 0302-9743 |
- Multi-objective optimization, NSGA-2, Preference-based learning, Solution selection
Research areas
ID: 223196345