A Unified Information-Theoretic Approach to Constrained Multi-Fidelity Multi-Objective Bayesian Optimization
CMF-PFEV is an information-theoretic method for constrained multi-fidelity multi-objective Bayesian optimization. It measures query utility by how much a proposed observation reduces uncertainty about the target-fidelity feasible Pareto frontier. This allows the algorithm to select queries that are cost-aware, handling objectives, constraints, and fidelity selection within a single criterion.