On the Potential of Automated Algorithm Configuration on Multi-Modal Multi-Objective Optimization Problems Rook, J Trautmann, Heike Bossek, Jakob Grimme, C Fieldsend, J Wagner, M. 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. Association for Computing Machinery 2022 info:eu-repo/semantics/conferenceObject doc-type:conferenceObject text http://purl.org/coar/resource_type/c_5794 https://ris.uni-paderborn.de/record/46305 Rook J, Trautmann H, Bossek J, Grimme C. On the Potential of Automated Algorithm Configuration on Multi-Modal Multi-Objective Optimization Problems. In: Fieldsend J, Wagner M, eds. <i>Proceedings of the Genetic and Evolutionary Computation Conference Companion</i>. GECCO ’22. Association for Computing Machinery; 2022:356–359. doi:<a href="https://doi.org/10.1145/3520304.3528998">10.1145/3520304.3528998</a> eng info:eu-repo/semantics/altIdentifier/doi/10.1145/3520304.3528998 info:eu-repo/semantics/altIdentifier/isbn/9781450392686 info:eu-repo/semantics/closedAccess