The removal of degeneracies for a physics-based particle packing simulation tended to improve performance as assessed by three different judging criteria: best in-sample predictions, repeated evaluations of the best suggested parameter set, and cross-validated model accuracy. Simultaneous use of multiple judging criteria may be more robust when making decisions related to problem representation for optimization tasks. The methods and analysis applied in this work are applicable to mate- rials science formulation problems such as composite materials, alloys, and composition-based materials discovery. We caution optimization practitioners in the physical sciences to carefully assess the influence of linear and non-linear constraints or reparameterizations on their search spaces, especially when expensive physics-based simulations or wetlab experiments and noisy objectives are involved. Pairing efficient search spaces with state-of-the-art optimization algorithms can improve optimization success relative to more standard approaches.