GeoPAS: Geometric Probing for Algorithm Selection in Continuous Black-Box Optimisation

• URL: http://arxiv.org/abs/2604.09095v2
• Date: Tue, 14 Apr 2026 02:07:35 GMT
• Title: GeoPAS: Geometric Probing for Algorithm Selection in Continuous Black-Box Optimisation
• Abstract summary: GeoPAS represents a problem instance by multiple coarse two-dimensional slices sampled across locations, orientations, and logarithmic scales.<n>A shared validity-aware convolutional encoder maps each slice to an embedding, conditions it on slice-scale and amplitude statistics.<n>GeoPAS improves over the single best solver under leave-instance-out, grouped random, and leave-problem-out evaluation.
• Score: 2.2060294178354307
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Automated algorithm selection in continuous black-box optimisation typically relies on fixed landscape descriptors computed under a limited probing budget, yet such descriptors can degrade under problem-split or cross-benchmark evaluation. We propose GeoPAS, a geometric probing approach that represents a problem instance by multiple coarse two-dimensional slices sampled across locations, orientations, and logarithmic scales. A shared validity-aware convolutional encoder maps each slice to an embedding, conditions it on slice-scale and amplitude statistics, and aggregates the resulting features permutation-invariantly for risk-aware solver selection via log-scale performance prediction with an explicit penalty on tail failures. On COCO/BBOB with a 12-solver portfolio in dimensions 2--10, GeoPAS improves over the single best solver under leave-instance-out, grouped random, and leave-problem-out evaluation. These results suggest that multi-scale geometric slices provide a useful transferable static signal for algorithm selection, although a small number of heavy-tail regimes remain and continue to dominate the mean. Our code is available at https://github.com/BradWangW/GeoPAS.

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