The following code snippet describes the setup for this example. We add an additional categorical parameter named "s", which comprises the general parameter options (in our case these are the different possible substrates for the Suzuki-Miyaura reaction encoded as Roman numerals). For general parameter problems, the acquisition type argument of the planner must be set to "general". Similar to the known constraints problems outlined in Section III A, the constructor takes an argument called general parameters, which must be a list of integers representing the parameter space indices of those parameters to be treated as general parameters. In our case, only the first parameter, "s", is a general parameter. Figure shows the results of a larger scale benchmark experiment on this problem. We compare the performance of the general parameter optimization stratetgy in Atlas to a strategy in which, for each set of recommended parameters, we measure the full objective, i.e. f (x, s) = 1 4 4 i=1 f (x, s i ). Effectively, the latter strategy must make 4 objective measurements for each set of recommened parameters, while the general parameter strategy must only make 1. The box plots in Figure show the hypervolume of solutions identified by each strategy averaged over the 4 reaction types. On average, the general parameter strategy produces larger hypervolumes, indicating superior multi-objective optimization perfomance.