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.
Original language | English |
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Title of host publication | GECCO 2022 Companion |
Subtitle of host publication | Proceedings of the 2022 Genetic and Evolutionary Computation Conference |
Place of Publication | New York, NY |
Publisher | Association for Computing Machinery |
Pages | 356-359 |
Number of pages | 4 |
ISBN (Electronic) | 978-1-4503-9268-6 |
DOIs | |
Publication status | Published - 9 Jul 2022 |
Event | Genetic and Evolutionary Computation Conference, GECCO 2022 - Boston, United States Duration: 9 Jul 2022 → 13 Jul 2022 https://gecco-2022.sigevo.org/HomePage |
Conference
Conference | Genetic and Evolutionary Computation Conference, GECCO 2022 |
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Abbreviated title | GECCO 2022 |
Country/Territory | United States |
City | Boston |
Period | 9/07/22 → 13/07/22 |
Internet address |
Keywords
- Configuration
- Multi-modality
- Multi-objective optimization
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