In the following, we evaluate the performance of EGBO on various cMOOPs, with qNEHVI-BO as baseline. We used unified non-dominated sorting genetic algorithm III (U-NSGA-III) as the EA component in the EGBO framework. For each problem, we first seek for a qualitative understanding of the optimization procedure. For synthetic problems, the optimization can be run several times and a probability density map is plotted to visualize the sampling distribution in the objective space, as proposed by Low et al. . In the real experimental campaign however, the optimization can only be run once. We then follow the optimization trajectory in the objective space in real time and track the location of new sampling points run by run.