which means that the candidates are more likely to be feasible even before being evaluated by the surrogate model. Therefore, EGBO is less sensitive to accurately learning the constraints. In practice, this could be useful in real-world experimentation scenarios, where infeasible solutions cannot be evaluated at all. As we described earlier in section 2, other implementations of constraint handling often involve training a separate model to classify feasibility, which could present a challenge especially during earlier iterations with limited data, and also in the case where the constraint boundary is very complex. EGBO (orange). The HV reference location is indicated by an orange star. We report the mean value of the metric (solid line) as well as its 95% confidence interval across 10 runs (shaded region).