{"type":"ScholarlyArticle","title":"Speeding Up the NSGA-II via Dynamic Population Sizes","authors":[{"name":"Benjamin Doerr","slug":"benjamin-doerr","given":"Benjamin","family":"Doerr","affiliation":""},{"name":"Martin S. Krejca","slug":"martin-s-krejca","given":"Martin S.","family":"Krejca","affiliation":""},{"name":"Simon Wietheger","slug":"simon-wietheger","given":"Simon","family":"Wietheger","affiliation":""}],"abstract":"Multi-objective evolutionary algorithms (MOEAs) are among the most widely and successfully applied optimizers for multi-objective problems. However, to store many optimal trade-offs (the Pareto optima) simultaneously, MOEAs are typically run with a large population of solution candidates. This slows down the algorithm and renders the choice of the population size a crucial design decision. In this work, we aim to overcome these difficulties by proposing the dynamic NSGA-II, a variant of the well-known NSGA-II that starts with a small initial population and doubles it after a user-specified number ? of function evaluations, up to a maximum size of ?ₘₐₓ. We prove that the dynamic NSGA-II with optimal parameters computes the Pareto front of the OneMinMax benchmark of size ? with high probability in O(? log² ?) function evaluations, which is considerably faster than the Θ(?² log ?) runtime of the static NSGA-II with optimal parameters. For the OneJumpZeroJump benchmark with gap size ?, we show a runtime of O(?^? log² ?), improving upon the known runtime of Θ(?^(? + 1)). We also propose a variant that uses the initial population size for a longer period and achieves slightly better performance. Finally, we show that a simple concurrent-run strategy turns our dynamic NSGA-II variants into parameter-less algorithms that exceed the above runtimes only by a logarithmic factor and hence still outperform the static NSGA-II by a factor of ͠Ω(?).","doi":"10.24963/ijcai.2026/698","url":"https://ijcai.org/proceedings/2026/698","pdf":"https://ijcai.org/proceedings/2026/0698.pdf","markdown":"https://ijcai.org/proceedings/2026/698.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/698","volume":{"slug":"2026","title":"Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2026","url":"https://ijcai.org/proceedings/2026/","year":2026,"location":"Bremen, Germany","dates":"15-21 August 2026","editors":"Diego Calvanese","isbn":"978-1-956792-09-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2026"},"section":"Search","track":"Main Track","number":698,"pages":{"first":6273,"last":6281},"keywords":["Search: Evolutionary computation"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}