While optimizations are successful, it is evident that for each optimization an almost infinite number of optimal starting conditions exist, especially taking into account that in the above example the polymer concentration was kept static for sake of simplicity. An example for such a set of optimal parameters is shown in Figure and Figure , including a Pareto front. The Pareto front represents the optimal trade-offs in multi-objective optimization, where no solution is superior in all objectives, defining the boundary between inferior and non-inferior solutions. For the construction of Figure and Figure optimizations were successively repeated for a target size of 200 nm on P6. In both cases, a tolerance of 10 nm was set for the optimization, and indeed averaging over all obtained results yields and almost perfect size average of 200 and 204 nm, respectively, with standard deviations of 13 and 7 nm only. For enhanced efficiency, a code explicitly designed for the Pareto front experiment was utilized. However, in order to obtain independent results, the algorithm does not store all data after reaching each size, treating each experiment as independent from the others.