Identifying solutions of interest for practical many-objective problems using recursive expected marginal utility
Hemant Kumar Singh, Tapabrata Ray, Tobias Rodemann, Markus Olhofer · Proceedings of the Genetic and Evolutionary Computation Conference Companion · 2019
Real-world problems often involve optimization of multiple conflicting objectives. Significant research has been directed recently towards development of multi-objective evolutionary algorithms that are scalable, i.e., able to deal with problems involving more than 3 objectives, commonly referred to as many-objective optimization problems. This has led to the emergence several new techniques that can deliver a set of trade-off solutions to approximate the Pareto optimal front of the problem. However, means to select solution(s) from this large trade-off set for final implementation/decision making has received relatively scarce attention. This paper aims to study and demonstrate the performance of recursive expected marginal utility (EMUr) approach for informed decisionmaking. Towards this goal, we apply the EMUr approach to identify solutions of interest for two practical examples and analyze the obtained set of solutions. The study highlights the desirable trade-off characteristics that the chosen solutions have over the rest of the trade-off set, highlighting its potential as a decision-making tool, especially in cases where other preference information or domain knowledge is unavailable.