On the Influence of the Feature Computation Budget on Per-Instance Algorithm Selection for Black-Box Optimization
Per-instance algorithm selection (PIAS) takes advantage of complementarity between a set of algorithms by deciding which algorithm to run on a given instance. This decision is based on features of the instances, which, in the context of black-box optimization (BBO), require a part of the optimization budget to be computed. This raises two questions: (a) from which fraction of the budget spent on feature computation does PIAS become worth it for BBO, and (b) which fraction of the budget optimizes the tradeoff between feature accuracy and PIAS performance. To this end, we perform a broad study where PIAS with varying sampling budgets for feature computation is compared to the single best al...
| Authors | Koen van der Blom, Diederick Vermetten |
|---|---|
| Primary category | cs.NE |
| Published | 6 May 2026 |
| Last updated at source | 5 October 2026 |
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