On the Influence of the Feature Computation Budget on Per-Instance Algorithm Selection for Black-Box Optimization
arXiv:2605.04954v2 Announce Type: replace-cross Abstract: 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
This paper addresses a fundamental optimization challenge within AI development, becoming more critical as algorithm selection becomes increasingly sophisticated and resource-intensive.
Understanding the efficiency of feature computation for algorithm selection is crucial for optimizing black-box optimization, a core component of many advanced AI systems.
Improved strategies for per-instance algorithm selection can lead to more efficient and adaptable AI systems, reducing computational waste and accelerating model development.
- · AI developers
- · Cloud computing providers
- · SaaS companies
- · AI-driven research
- · Inefficient AI frameworks
- · Companies with high compute costs
More efficient AI development processes will emerge, requiring less computational budget for specific tasks.
This efficiency will enable more complex black-box optimization problems to be tackled, expanding the scope of AI applications.
Reduced compute requirements could democratize access to advanced AI development, fostering innovation in smaller organizations.
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Read at arXiv cs.LG