用于连续黑盒优化算法选择的几何探测框架

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Abstract:Automated algorithm selection for continuous black-box optimization depends on what information is acquired from a problem under a limited probing budget and how that information is represented. We introduce a geometric probing framework that samples multi-scale two-dimensional restrictions across location, orientation, and scale, and encodes their normalized objective-value maps with validity-aware convolutional processing and permutation-invariant aggregation. We compare our method with classical ELA and Deep-ELA under matched budgets, within-problem and problem-level transfer, fusion, representation, and budget analyses. We further disentangle probe acquisition from probe processing by controlled ablation. The results show that the proposed visual representation exposes solver-performance information complementary to ELA-family features and retains a relative advantage for relative expected runtime under problem-level transfer, while greater coverage, feature richness, or predictive accessibility alone does not guarantee better selection.

Comments:	21 pages, 15 figures, 4 tables; extended journal version of a GECCO Companion 2026 paper; code available at this https URL
Subjects:	Machine Learning (cs.LG); Optimization and Control (math.OC)
Cite as:	arXiv:2604.09095 [cs.LG]
	(or arXiv:2604.09095v4 [cs.LG] for this version)
	https://doi.org/10.48550/arXiv.2604.09095 arXiv-issued DOI via DataCite

Submission history

From: Jiabao Brad Wang [view email]
[v1] Fri, 10 Apr 2026 08:24:37 UTC (21,488 KB)
[v2] Tue, 14 Apr 2026 02:07:35 UTC (21,488 KB)
[v3] Thu, 21 May 2026 16:13:41 UTC (20,646 KB)
[v4] Wed, 7 Oct 2026 08:41:59 UTC (4,658 KB)