While performing validation via repeated runs of the same parameters is the most robust of the three sets of summarized results (Figure ) in terms of comparing different search spaces, it is also the most expensive because it requires additional objective function evaluations. When performing validation through repeated runs of best in-sample predictions for objective functions with large variance is not feasible during the optimization design phase, we encourage optimization practitioners to use multiple "notions-of-best" to assess superiority of one model configuration vs. another. While there are differences between the ranking trends of best insample predictions (Figure ) and the LOO-CV scaled errors (Figure ), the search space with only order constraints ("order") performs well in both of these cases. Interestingly, this search space is also the one that performs the best in the high-fidelity notion of best, the validated volume fractions.