Evolutionary Multiobjective Optimization: from Practice to Theory

Several questions directly arise from practical applications such as (1) how to effectively incorporate the preferences of a decision maker into the search process, (2) how the decision maker should be involved in interactive optimization if some preferences cannot be formalized mathematically, or more generally, (3) how problems with many, i.e., more than 5 objectives can be tackled in terms of visualization, decision making, and search. One of the main challenges in the field of evolutionary multiobjective optimization is how to develop efficient and effective optimization methods addressing the aforementioned questions.

In this talk, I will address the questions (1) and (3) both from a practical and from a theoretical point-of-view. The talk is divided into three parts. First, I will explain how it is possible to include preferences into the search by means of weighted hypervolume indicators. Second, I will present how the automated reduction of objectives can assist both in decision making and search by means of a radar waveform optimization problem. Last, I will indicate some interesting open questions in the field of evolutionary multiobjective optimization.