@inproceedings{48868,
  author       = {{Bossek, Jakob and Neumann, Aneta and Neumann, Frank}},
  booktitle    = {{Proceedings of the Genetic and Evolutionary Computation Conference Companion}},
  isbn         = {{978-1-4503-9268-6}},
  pages        = {{824–842}},
  publisher    = {{Association for Computing Machinery}},
  title        = {{{Evolutionary Diversity Optimization for Combinatorial Optimization: Tutorial at GECCO’22, Boston, USA}}},
  doi          = {{10.1145/3520304.3533626}},
  year         = {{2022}},
}

@inproceedings{48896,
  abstract     = {{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.}},
  author       = {{Rook, Jeroen and Trautmann, Heike and Bossek, Jakob and Grimme, Christian}},
  booktitle    = {{Proceedings of the Genetic and Evolutionary Computation Conference Companion}},
  isbn         = {{978-1-4503-9268-6}},
  keywords     = {{configuration, multi-modality, multi-objective optimization}},
  pages        = {{356–359}},
  publisher    = {{Association for Computing Machinery}},
  title        = {{{On the Potential of Automated Algorithm Configuration on Multi-Modal Multi-Objective Optimization Problems}}},
  doi          = {{10.1145/3520304.3528998}},
  year         = {{2022}},
}