Constraint-Bound Agnostic Bayesian Optimization: One Model for All Thresholds
Expensive constrained optimization problems in real-world industry design often involve constraint thresholds that are difficult to determine in advance. Engineers may need to adjust constraint thresholds to explore different feasibility-performance trade-offs, requiring solutions under a wide range of threshold settings. However, existing constrained Bayesian optimization methods treat each threshold configuration independently, leading to repeated optimization and failing to exploit the shared relationship among continuously varying thresholds. To address this challenge, we propose constraint-bound agnostic Bayesian optimization (CBA-BO), a learning-based framework that learns a paramet...
| Authors | Jin Wang, Xi Lin, Handing Wang |
|---|---|
| Primary category | cs.NE |
| Published | 26 July 2026 |
| Last updated at source | 26 July 2026 |
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