8, 9, and Table . In achieving the best objective function values within a specified budget, EDBO_1 outperforms all other methods, while PWAS is comparable with that of Hyperopt (see Fig. ) and EDBO_2 (see Fig. ). However, the effectiveness of EDBO varies depending on the number of discretization steps considered. As evidenced in Fig. , the performance of EDBO improves with an increase in discretization steps, showing that only EDBO_1 outperforms PWAS. Although the differences in objective function values obtained by EDBOs and PWAS are marginal, the number of discretization steps required to achieve a particular quality of solution is unknown and there is no systematic method of determining appropriate value. Furthermore, the increase in the number of discretization steps can result in higher computational cost, particularly with a higher number of continuous variables. This is reflected in Table , where the CPU time for EDBO and BoTorch are significantly higher than that of other methods. Despite that, we acknowledge that the time required for actual experiments or simulations outweighs the computational overhead in many cases. Therefore, the difference in CPU time may not always be significant. Nevertheless, there are some scenarios where computational efficiency becomes critical, such as in applications requiring rapid feedback loops, including flow chemistry . Additionally, selecting an appropriate discretization scheme that guarantees improved performance a priori may not be straightforward, particularly when the number of continuous variables and their ranges grow. Evolutionary-assisted surrogatebased methods such as the one proposed in Low et al.