While we have achieved improved parameter sets with simulation-based regularized least-squares optimization, it has significant limitations. A major drawback is that, regardless of the optimization algorithm used, the number of objective function evaluations possible is limited by the computational cost of simulations. This limits the number of parameter sets that can be considered during optimization, making it difficult to explore high-dimensional and complex parameter spaces. This also necessitates the use of cheaper local optimization methods such as L-BFGS-B with termination after a set number of steps . When coupled with a regularization term in the objective function, included both to ensure the stability of the optimization and to guard against overfitting , local optimization methods have a high probability of remaining in any local minima dictated by its initial values. This means that our ability to explore new areas of parameter space that may provide significant improvement is blunted because of the expense of evaluating the objective.