Uncertainty-Aware Offline Data-Driven Multi-Objective Optimization

In offline data-driven Multi-Objective Optimization (MOO), optimization is performed using surrogate models trained only on an offline dataset. When only limited data are available, surrogate models may exhibit substantial epistemic uncertainty and prediction errors. These errors can lead to incorrect dominance judgments and mislead the search process. Existing uncertainty-aware offline data-driven Multi-Objective Evolutionary Algorithms (MOEAs) often rely on Gaussian Process Regression (GPR), limiting their direct applicability to other surrogate models. Meanwhile, generative-based methods may struggle to explore beyond the offline data distribution when it is far from the true Pareto front. We propose EBU-DR, which combines an Empirical-Bayes Uncertainty (EBU) adjustment with a Dual-Ranking (DR) strategy for evolutionary survival selection. EBU uses five-fold out-of-fold predictions to estimate the shrinkage parameters. When predictive signal is sufficient, it shrinks predictions toward the offline objective mean; otherwise, uncertainty estimates are used directly. DR performs non-dominated sorting on both surrogate-predicted and uncertainty-aware objective values, while crowding distances are calculated using only the surrogate-predicted objectives. We evaluate EBU-DR with four surrogate configurations across 29 benchmark problems and different offline dataset sizes, comparing it with representative offline MOO methods.