In Figure the evolution of the dominated hypervolume, maximum geometric mean and internal similarity of the NSGA-II, NSGA-III, and GB-EPI algorithms is shown for two representative benchmarks (Cobimentib and Fexofenadine). Throughout the computational experiments GB-EPI, which optimises directly for the geometric mean, is used as a baseline comparison method. For both benchmarks, NSGA-II and NSGA-III successfully out- The geometric mean follows trends similar to the dominated hypervolume in the benchmarks. However, the values of the maximal geometric mean lie close to each other and the 95% confidence interval of GB-EPI overlaps with NSGA-II and NSGA-III during the latter stages of the Cobimentib task. An overview of the results for the multi-objective benchmarks is shown in Table in terms of averages and standard deviations. NSGA-II and NSGA-III perform better than the baseline on each of the benchmarks for both dominated hypervolume and maximum geometric mean. For the Fexofenadine and Pioglitazone benchmarks, GB-EPI lies within one standard deviation of either NSGA-II or NSGA-III for both metrics. In contrast to single objective optimisation problems, where a lower internal similarity has been regarded as beneficial, for multi-objective optimisation the algorithms which encourage greater internal similarity are better performing.