Interactive Robust Multiobjective Optimization Driven by Decision Rule Preference Model

Authors:
Roman Słowiński
Affiliations:
Institute of Computing Science, Poznań University of Technology, Poznań and Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland 00-441
Venue:
MDAI '09 Proceedings of the 6th International Conference on Modeling Decisions for Artificial Intelligence
Year:
2009

Citing 3
Cited 0

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Dominance-Based Rough Set Approach to Interactive Multiobjective Optimization

Multiobjective Optimization
Interactive Multiobjective Optimization from a Learning Perspective

Multiobjective Optimization

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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).