On the Potential of Multi-objective Automated Algorithm Configuration on Multi-modal Multi-objective Optimisation Problems

The complexity of Multi-Objective (MO) continuous optimisation problems arises from a combination of different characteristics, such as the level of multi-modality. Earlier studies revealed that there is a conflict between solver convergence in objective space and solution set diversity in the decision space, which is especially important in the multi-modal setting. We build on top of this observation and investigate this trade-off in a multi-objective manner by using multi-objective automated algorithm configuration (MO-AAC) on evolutionary multi-objective algorithms (EMOA). Our results show that MO-AAC is able to find configurations that outperform the default configuration as well as configurations found by single-objective AAC in regards to objective space convergence and diversity in decision space, leading to new recommendations for high-performing default settings.