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dc.contributor.authorAggarwal, Manish
dc.contributor.authorHeinermann, Justin
dc.contributor.authorOehmcke, Stefan
dc.contributor.authorKramer, Oliver
dc.date.accessioned2019-05-24T21:28:41Z
dc.date.available2019-05-24T21:28:41Z
dc.date.issued2017
dc.identifier.citationAggarwal, M., Heinermann , J., Oehmcke, S., & Kramer 0 . (2017) . Preferences-based choice prediction in evolutionary multi­ objectiveoptimization.InG.Squillero,& K.Sim (Eds),ApplicationsofEvolutionaryComputation.EvoA pplications2017, Cha m: Springer, (pp.715-724). Retrieved from htt ps:// li nk. springer.com/chapter/10.1007/978-3-319-55849-3_46
dc.identifier.urihttp://hdl.handle.net/11718/21931
dc.description.abstractEvolutionary multi-objective algorithms (EMOAs) of the type of NSGA-2 approximate the Pareto-front, after which a decision-maker (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.en_US
dc.publisherSpringeren_US
dc.subjectMulti-objective optimizationen_US
dc.subjectNSGA-2en_US
dc.subjectPreference-based learningen_US
dc.subjectSolution selectionen_US
dc.titlePreferences-based choice prediction in evolutionary multi-objective optimizationen_US
dc.title.alternativeApplications of Evolutionary Computationen_US
dc.typeArticleen_US


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