Hardness of Multi-Objective (MO) continuous optimization problems results from an interplay of various problem characteristics, e.g. the degree of multi-modality. We present a benchmark study of classical and diversity focused optimizers on multi-modal MO problems based on automated algorithm configuration. We show the large effect of the latter and investigate the trade-off between convergence in objective space and diversity in decision space.
|Title of host publication||GECCO '22|
|Subtitle of host publication||Proceedings of the Genetic and Evolutionary Computation Conference Companion|
|Number of pages||4|
|Publication status||Published - 9 Jul 2022|