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A Confidence-Driven Evolutionary Algorithm for Noisy Optimization with Joint Chance Constraints — arXiv:2609.21318

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.21318
License (as archived): CC0-1.0
Archived: 2026-10-10T02:45:55.652Z
SHA-256 of the archived text: 1eb677e8b1f016306611eb0b6125c7cd1d97a1cd5d448a4a762d6fcf3eac2873
Stable URL of this page: https://metraipolis.com/documento/dc_068cbcedc052

How to cite

A Confidence-Driven Evolutionary Algorithm for Noisy Optimization with Joint Chance Constraints — arXiv:2609.21318 — arXiv (en), https://arxiv.org/abs/2609.21318 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_068cbcedc052 (Metraipolis archive dc_068cbcedc052, sha256 1eb677e8b1f016306611eb0b6125c7cd1d97a1cd5d448a4a762d6fcf3eac2873)

BibTeX

@misc{dc068cbcedc052,
  title = {A Confidence-Driven Evolutionary Algorithm for Noisy Optimization with Joint Chance Constraints — arXiv:2609.21318},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.21318},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_068cbcedc052; sha256 1eb677e8b1f016306611eb0b6125c7cd1d97a1cd5d448a4a762d6fcf3eac2873},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.21318. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Many real-world optimization problems involve noisy objective evaluations and probabilistic constraints, particularly in the form of joint chance constraints, which are computationally expensive to evaluate. In this work, we propose CR-EA-C, a confidence-driven evolutionary algorithm for solving noisy black-box optimization problems under joint chance constraints. CR-EA-C introduces three key components: (1) analytical feasibility estimation for joint chance constraints, (2) a pairwise statistical ranking mechanism for robust comparison under noise, and (3) a modified infeasibility-driven survival strategy to accelerate convergence. These components enable statistically reliable decision-making while improving the efficiency of function evaluations. The proposed method is evaluated against four recent metaheuristic algorithms under various uncertainty distributions. Furthermore, its practical effectiveness is also assessed on two additional real-world optimization problems and compared with conventional static sampling methods. Experimental results show that CR-EA-C consistently satisfies the prescribed joint chance constraints while achieving competitive objective values overall. This demonstrates that CR-EA-C is an effective general-purpose approach for noisy optimization.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-21 in cs.NE).
Categorie arXiv: Neural and Evolutionary Computing (cs.NE).
Pagina del lavoro: https://arxiv.org/abs/2609.21318 — 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 2026-10-10T02:23:07.842Z dalla pagina https://arxiv.org/catchup/cs.NE/2026-09-21?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).