The EGBO framework is illustrated in Fig. , left. Once the data set is updated with newly sampled objective and constraint values, two processes occur at the same time. On one side, a Gaussian Process (GP) surrogate model is trained with the data set and is used to run the qNEHVI optimizer to suggest 4 new candidates following its implementation in BoTorch . In parallel, and independently, parents are selected from the non-dominated solutions of the original data set, with 256 new children candidates proposed by the EA. These two sets of new candidates (4 BO + 256 EA) are then combined where we greedily maximize for EHVI acquisition value, taking the 4 overall best candidates for evaluation.