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Multitask Pareto Optimization for Monotone Submodular Problems with Dynamic Constraints — arXiv:2608.10425

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.10425
License (as archived): CC0-1.0
Archived: 2026-10-19T06:36:38.607Z
SHA-256 of the archived text: 7e0590a8e9c70c37f7008d68c91f1d21ef09c1c75a868d9f8344d4cc4d11fe45
Stable URL of this page: https://metraipolis.com/documento/dc_cba768b84a67

How to cite

Multitask Pareto Optimization for Monotone Submodular Problems with Dynamic Constraints — arXiv:2608.10425 — arXiv (en), https://arxiv.org/abs/2608.10425 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_cba768b84a67 (Metraipolis archive dc_cba768b84a67, sha256 7e0590a8e9c70c37f7008d68c91f1d21ef09c1c75a868d9f8344d4cc4d11fe45)

BibTeX

@misc{dccba768b84a67,
  title = {Multitask Pareto Optimization for Monotone Submodular Problems with Dynamic Constraints — arXiv:2608.10425},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.10425},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_cba768b84a67; sha256 7e0590a8e9c70c37f7008d68c91f1d21ef09c1c75a868d9f8344d4cc4d11fe45},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.10425. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Evolutionary multitasking is a recent approach that solves multiple related optimization problems within a single evolutionary run, rather than addressing each problem separately. We consider monotone submodular optimization problems with dynamic knapsack constraints and study a multitasking formulation in which all tasks share a common monotone submodular function $f$, but differ in their constraints. We focus on the case where elements within each constraint have uniform cost and show that this structure leads to small Pareto fronts in the multitasking formulation. This enables solution sharing across tasks and can improve performance compared to running standard evolutionary approaches independently, depending on the constraint regime. Using rigorous runtime analysis, we analyze the expected time until the proposed multitasking algorithms obtain a $(1 - 1/e)$-approximation for each task. Experimental results for the Maximum Coverage problem complement the theoretical analysis and provide further insight into the practical behavior of the approach across different budget settings.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-08-12 in cs.NE).
Categorie arXiv: Neural and Evolutionary Computing (cs.NE).
Pagina del lavoro: https://arxiv.org/abs/2608.10425 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-19T06:27:49.000Z dalla pagina https://arxiv.org/catchup/cs.NE/2026-08-12?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).