On the elicitation of indirect preferences in interactive evolutionary multiple objective optimization

We consider essential challenges related to the elicitation of indirect preference information in interactive evolutionary algorithms for multiple objective optimization. The methods in this stream use holistic judgments provided by the Decision Maker (DM) to progressively bias the evolutionary search toward his/her most preferred region in the Pareto front. We enhance such an interactive process using three targeted developments and illustrate their efficiency in the context of a decomposition-based evolutionary framework. Firstly, we present some active learning strategies for selecting solutions from the current population that should be critically compared by the DM. These strategies implement the paradigm of maximizing the potential information gain derived from the DM's answer. Secondly, we discuss the procedures for deciding when the DM should be questioned for preference information. In this way, we refer to a more general problem of distributing the DM's interactions with the method in a way that ensures sufficient evolutionary pressure. Thirdly, we couple the evolutionary schemes with different types of indirect preferences, including pairwise comparisons, preference intensities, best-of-k judgments, and complete orders of a small subset of solutions. A thorough experimental analysis indicates that the three introduced advancements have a positive impact on the DM-perceived quality of constructed solutions.