Co-evolution improves the efficiency of preference learning methods when the Decision Maker's aspirations develop over time

Abstract

This paper's research scope is interactive evolutionary multiple objective optimization founded on the preference learning paradigm. It concerns a scenario in which the Decision Maker's (DM's) aspirations develop over time. In this view, the interactive method may be forced to occasionally re-learn the DM's value system and, thus, re-orient the search during optimization. In preliminary studies, we observed that although a satisfactory recommendation can ultimately be discovered, it is often attainable with more significant computational power. To resolve this issue, we propose a co-evolutionary method, evolving sub-populations that approximate the Pareto front or align with the DM's preferences. This diversifies the maintained solution set, which is useful when re-understanding the DM's aspirations during interactions. Also, it helps reallocate the preference-driven sub-population quickly, avoiding an extensive computational burden. We demonstrate the usefulness of such hybridization in a series of extensive experiments that involve different test problems, numbers of objectives, and behavioral models of the Decision Maker.