Learning of a Decision-Maker's Preference Zone With an Evolutionary Approach.

ABSTRACT

A new evolutionary-learning algorithm is proposed to learn a decision maker (DM)'s best solution on a conflicting multiobjective space. Given the exemplary pairwise comparisons of solutions by a DM, we learn an ideal point (for the DM) that is used to evolve toward a better set of solutions. The process is repeated to get the DM's best solution. The comparison of solutions in pairs facilitates the process of eliciting training information for the proposed learning model. Experimental study on standard multiobjective data sets shows that the proposed method accurately identifies a DM's preferred zone in relatively a few generations and with a small number of preferences. Besides, it is found to be robust to inconsistencies in the preference statements. The results obtained are validated through a variant of the established NSGA-2 algorithm.