EMOSOR: Evolutionary multiple objective optimization guided by interactive stochastic ordinal regression

We propose a family of algorithms, called EMOSOR, combining Evolutionary Multiple Objective Optimization with Stochastic Ordinal Regression. The proposed methods ask the Decision Maker (DM) to holistically compare, at regular intervals, a pair of solutions, and use the Monte Carlo simulation to construct a set of preference model instances compatible with such indirect and incomplete information. The specific variants of EMOSOR are distinguished...