Overall, our pre-repair EGBO approach effectively combines the ability to deal with cMOOPs. The concept of integrating a gradient-based approach and EAs has been previously explored by Hickman et al. . This constraint handling method is close to the approach reported in section 3.3, as the objectives are only evaluated in the feasible region. However, their approach uses the constraint as a stop criterion for the gradient-based approach. As for the genetic algorithm, infeasible children are projected onto the feasible boundary using binary search. This post-repair approach is more reliable than the one using a repair operator, as the binary search simply looks for the best feasible point located between the feasible parent and the infeasible offspring without bias. Although these two methods are able to converge towards the constrained PF, EGBO with pre-repair demonstrates a more robust approach that can cater to unknown constraints, as discussed above. Furthermore, Hickman's approach is limited to known constraints, where suggested infeasible samples are rejected, while EGBO is also designed to handle unknown constraints, as demonstrated in section 3.2.