Hot off the Press: Near-Tight Runtime Guarantees for Many-Objective Evolutionary Algorithms
Despite significant progress in the field of mathematical runtime analysis of multi-objective evolutionary algorithms (MOEAs), the performance of MOEAs on discrete many-objective problems is little understood. In particular, the few existing performance guarantees for classic MOEAs on classic benchmarks are all roughly quadratic in the size of the Pareto front.
Authors
- Simon Wietheger (ORCID: https://orcid.org/0000-0002-0734-0708)
- Benjamin Doerr (ORCID: https://orcid.org/0000-0002-9786-220X)
Institutions
- TU Wien (AT)
Publication Details
- Journal
- Proceedings of the Genetic and Evolutionary Computation Conference Companion
- Published
- 2025-07-14
- DOI
- https://doi.org/10.1145/3712255.3734251
- Primary Topic
- Advanced Multi-Objective Optimization Algorithms
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Austrian Science Fund
- Fondation Mathématique Jacques Hadamard