Exploring the Explainable Aspects and Performance of a Learnable Evolutionary Multiobjective Optimization Method

Abstract

Multiobjective optimization problems have multiple conflicting objective functions to be optimized simultaneously. The solutions to these problems are known as Pareto optimal solutions, which are mathematically incomparable. Thus, a decision maker must be employed to provide preferences to find the most preferred solution. However, decision makers often lack support in providing preferences and insights in exploring the solutions available.

We explore the combination of learnable evolutionary models with interactive indicator-based evolutionary multiobjective optimization to create a learnable evolutionary multiobjective optimization method. Furthermore, we leverage interpretable machine learning to provide decision makers with potential insights about the problem being solved in the form of rule-based explanations. In fact, we show that a learnable evolutionary multiobjective optimization method can offer advantages in the search for solutions to a multiobjective optimization problem. We also provide an open source software framework for other researchers to implement and explore our ideas in their own works.

Our work is a step towards establishing a new paradigm in the field on multiobjective optimization: explainable and learnable multiobjective optimization. We take the first steps towards this new research direction and provide other researchers and practitioners with necessary tools and ideas to further contribute to this field.