Constrained Bayesian Optimization with Feasibility-Infeasibility Weighted Improvement Criterion
Abstract
In Bayesian optimization, Expected Improvement (EI) is widely used for unconstrained optimization but lacks effective handling of constraints. Existing approaches modify EI by incorporating feasibility probabilities, requiring an initial feasible point, and often restricting exploration to the feasible region. This article introduces a novel improvement-based acquisition function designed to address these limitations. The proposed function strikes a balance between exploration and exploitation across both feasible and infeasible regions. We evaluate our framework against state-of-the-art methods on four benchmark problems and apply it to groundwater remediation in hydrology and hyperparameter tuning of neural networks.
Fields of Study & Subjects
Risk and Portfolio Optimization
Advanced Bandit Algorithms Research
Advanced Multi-Objective Optimization Algorithms
Publication details
- Journal / source
- Journal of Computational and Graphical Statistics
- Publisher
- Informa UK Limited
- Volume
- 35
- Issue
- 3
- Pages
- 1249–1261
- Publication date
- 7 January 2026
- Language
- EN