PIAS in black-box optimization remains beneficial versus the single best algorithm for most tested cases even with 25% budget spent on features, and feature computation explains about 20% of the average performance gap to the virtual best solver.
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Cross-benchmark study tests transfer of AS models between BBOB/CEC suites and robotics/UAV problems, identifying generalization failures in realistic settings.
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On the Influence of the Feature Computation Budget on Per-Instance Algorithm Selection for Black-Box Optimization
PIAS in black-box optimization remains beneficial versus the single best algorithm for most tested cases even with 25% budget spent on features, and feature computation explains about 20% of the average performance gap to the virtual best solver.