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• W60207871 abstract "The objective functions in multiobjective optimization problems are often non-linear, noisy, or not available in a closed form and evolutionary multiobjective optimization (EMO) algorithms have been shown to be well applicable in this case. Here, our objective is to facilitate interactive decision making by saving function evaluations outside the “interesting” regions of the search space within a hypervolume-based EMO algorithm. We focus on a basic model where the Decision Maker (DM) is always asked to pick the most desirable solution among a set. In addition to the scenario where this solution is chosen directly, we present the alternative to specify preferences via a set of so-called comparative preference statements. Examples on standard test problems show the working principles, the competitiveness, and the drawbacks of the proposed algorithm in comparison with the recent iTDEA algorithm." @default.
• W60207871 created "2016-06-24" @default.
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• W60207871 date "2014-01-01" @default.
• W60207871 modified "2023-09-26" @default.
• W60207871 title "Using Comparative Preference Statements in Hypervolume-Based Interactive Multiobjective Optimization" @default.
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• W60207871 doi "https://doi.org/10.1007/978-3-319-09584-4_13" @default.
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