Abstract
Interactive procedures for MultiObjective Optimization (MOO) consist of a sequence of steps alternating calculation of a sample of non-dominated solutions and elicitation of preference information from the Decision Maker (DM). We consider three types of procedures, where in preference elicitation stage, the DM is just asked to indicate which solutions are relatively good in the proposed sample. In all three cases, the preference model is a set of “if . . . , then . . .” decision rules inferred from the preference information using the Dominance-based Rough Set Approach (DRSA) (3; 4; 11).
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Słowiński, R. (2009). Interactive Robust Multiobjective Optimization Driven by Decision Rule Preference Model. In: Torra, V., Narukawa, Y., Inuiguchi, M. (eds) Modeling Decisions for Artificial Intelligence. MDAI 2009. Lecture Notes in Computer Science(), vol 5861. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04820-3_1
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DOI: https://doi.org/10.1007/978-3-642-04820-3_1
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