For the CMA-ES optimisation procedure, 50 % of the total population were defined as parents, and an initial standard deviation of σ = 0.1 was chosen. The generated output from the evolutionary algorithms was compared with the data of the sequential one-parameter parabolic interpolation (SOPPI) approach. In the latter, a step size of ± 10 % of the parameter range was adopted for the parabolic fit. In all methods, constraints were applied to keep the parameter search space within the specified boundaries. In the case of the DE approach, altering the differential evolution strategy can overcome this issue (see SI -Table .13). The rand1bin and rand2bin strategies utilise random parent vectors in the generation of the mutation vector. This improves the exploration capability of the algorithm but may slow down convergence. Further, the rand2bin strategy introduces an additional difference vector, which enhances the perturbation. For the strategies best1bin and best2bin, the best solutions of the parent population are used to generate the mutation vector, which allows faster convergence to the optimal solution, but bears the risk of getting stuck in a local minimum in the case of multimodal functions . As shown in Table .13, applying the rand1bin or rand2bin differential evolution strategy yields a robust identification of the global solution of Ackley's function. The performance of the SOPPI approach can be improved by decreasing the step size gradually during the optimisation process. In our case, an initial step length of δ = 0.1 or δ = 0.05 and a reduction of 1 % every 10 iterations (down to a minimum value of δ = 0.005) provided the model's best solution consistently (see SI -Table .23).