Interactive Evolutionary Multiobjective Optimization using Dominance-based Rough Set Approach

Abstract

We present basic ideas related to application of Dominance-based Rough Set Approach (DRSA) in interactive Evolutionary Multiobjective Optimization (EMO). In the proposed methodology, the preference information elicited by the decision maker in successive iterations consists in sorting some solutions in the current population into “relatively good” and “others”, or in comparing some pairs of solutions with respect to preference. The “if ..., then ...” decision rules are then induced from this preference information using Dominance-based Rough Set Approach (DRSA). These rules are used within EMO in order to focus on populations of solutions satisfying the preferences of the decision maker, speeding up convergence to the most preferred region of the Pareto-front. The resulting interactive schemes, corresponding to the two types of preference information, are called DRSA-EMO and DRSA-EMO-PCT, respectively. The proposed methodology permits also to take into account robust concerns in multiobjective optimization.