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How Sequential Algorithm Portfolios can benefit Black Box...
[Submitted on 23 Jan 2026 (v1), last revised 9 Sep 2026 (this ve · 2026-01-24 · via cs.NE updates on arXiv.org

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Abstract:In typical black-box optimization applications, the available computational budget is often allocated to a single algorithm, typically chosen based on user preference with limited knowledge about the problem at hand or according to some expert knowledge. However, we show that splitting the budget across several algorithms yield significantly better results. This approach benefits from both algorithm complementarity across diverse problems and variance reduction within individual functions, and shows that algorithm portfolios do NOT require parallel evaluation capabilities. To demonstrate the advantage of sequential algorithm portfolios, we apply it to the COCO data archive, using over 200 algorithms evaluated on the BBOB test suite. The proposed sequential portfolios consistently outperform single-algorithm baselines, achieving relative performance gains of over 14%, and offering new insights into restart mechanisms and potential for warm-started execution strategies.

Submission history

From: Catalin-Viorel Dinu [view email]
[v1] Fri, 23 Jan 2026 17:02:22 UTC (445 KB)
[v2] Wed, 9 Sep 2026 13:52:54 UTC (552 KB)