Geometric Probing for Algorithm Selection in Continuous Black-Box Optimization
The study introduces a geometric probing framework to select algorithms for continuous black-box optimization by sampling multi-scale restrictions across location, orientation, and scale. It develops a visual representation that encodes these samples using validity-aware processing and permutation-invariant aggregation. The findings show this representation offers complementary performance information to existing methods, particularly benefiting relative expected runtime under problem-level transfer.