In contrast, evolutionary algorithms feature excellent exploration capabilities within the model parameter space and can be parallelised readily with near-linear scaling. These algorithms demonstrate their robustness in finding good fitting solutions in sampling search spaces of varying complexity and dimensionality. Also, the concurrent fitting of parameters provides an enhanced mapping of the existing parameter correlations. The extensive sampling of the model parameter space minimises the risk of getting caught in a local minimum and limits the influence of the parameter set's initial guess on the optimisation performance.