Solution Subset Selection for Final Decision Making in Evolutionary Multi-Objective Optimization
This work tackles the practical issue of reducing decision-maker burden in multi-objective optimization, but it is incremental as it builds on existing indicator-based methods.
The paper addresses the problem of selecting a small subset of solutions from a large set obtained in evolutionary multi-objective optimization to aid human decision-making, showing that their proposed approach, based on an expected loss function equivalent to the IGD plus indicator, performs competitively in experiments.
In general, a multi-objective optimization problem does not have a single optimal solution but a set of Pareto optimal solutions, which forms the Pareto front in the objective space. Various evolutionary algorithms have been proposed to approximate the Pareto front using a pre-specified number of solutions. Hundreds of solutions are obtained by their single run. The selection of a single final solution from the obtained solutions is assumed to be done by a human decision maker. However, in many cases, the decision maker does not want to examine hundreds of solutions. Thus, it is needed to select a small subset of the obtained solutions. In this paper, we discuss subset selection from a viewpoint of the final decision making. First we briefly explain existing subset selection studies. Next we formulate an expected loss function for subset selection. We also show that the formulated function is the same as the IGD plus indicator. Then we report experimental results where the proposed approach is compared with other indicator-based subset selection methods.