"There are many areas in physics where parametric optimization studies are required to unravel the essentials of complex and high-dimensional systems.While finding optimal solutions can be likened to root-finding, figures of merit are typically calculated by a program-code rather than analytic mathematical formulae. As this code may involve the processing of large amounts of data while using complex algorithms in order to derive an evaluation, assigning a figure of merit to a single set of input parameters may last anywhere from seconds to hours. Parametric optimization in physics research thus benefits particularly from parallelization, using the combined processing power of large compute clusters, with hundreds or thousands of CPU cores to handle the tens of thousands of evaluations typically performed in an optimization run. One particularly complex problem in theoretical particle physics is the analysis of QCD lattice data in terms of the relevant and effective degrees of freedom. To actually perform such studies can be quite a computational challenge. For instance a recent analysis of the baryon masses as measured on various QCD lattices is based on eight coupled non-linear equations that must be solved for each lattice ensemble considered and any trial set of the low-energy parameters. In elementary particle physics, typically the MINUIT library would be taken in the context of the ROOT framework in order to tackle such a task. However, our attempts to use a gradient approach as offered by MINUIT did not lead to any competitive result. We then chose to apply the evolutionary algorithm provided by the Geneva library collection instead, which supports distributed optimization out of the box. Addressing this task by using Geneva was far more successful. We submitted typical Geneva runs with a population size of 8000 individuals on 700 parallel CPU cores. The runtime of a fit job lasted about a week or more. Using the Geneva library collection has thus allowed us to perform research beyond what was so far possible with the tools we were up to then aware of. We are grateful to Gemfony Scientific for the comprehensive help they provided with regards to the usage of their library and and that their framework has been extended also by additional functionality in order to better adapt to our needs." (Prof. Dr. Matthias F.M. Lutz, Technische Universität Darmstadt, and GSI Helmholtzzentrum für Schwerionenforschung GmbH, March 2018)