Common optimization strategies to solve cMOOPs include Bayesian Optimization (BO), which relies on (1) a surrogate model to learn the function 𝒇 linking the decision and objective spaces, (2) an acquisition function to predict the gain of a given candidate(s), and finally (3) an internal optimization method to find the candidate(s) with the highest acquisition function for evaluation. Evolutionary Algorithms (EA), in contrast, are data-centric methods without predictive models. A set of most optimal parents is selected to generate a population of children via crossover and mutation . These children are 'genetically close' to the most optimal solutions, and in practice evolve towards the PF by virtue of selection pressure. Daulton et al. , recently proposed a way to combine expected HV improvement (EHVI) with gradient-based methods to enable efficient and effective optimization, robust to many objectives and noisy evaluations. HV improvement as a strategy has been shown to be robust in multi-objective optimization compared to decomposition or scalarization based methods which may require more careful hyperparameter tuning . Furthermore, qNEHVI-BO has been demonstrated to outperform other well-performing MOO algorithms including Thompson sampling efficient multi-objective optimization (TSEMO) and diversity-guided efficient multi-objective optimization (DGEMO) .