The choice of the number of datapoints (obtained via random selection) for initializing the closed-loop cycle impacts the preliminary surrogate model's prediction performance. While more initial datapoints could be considered as advantageous to train more accurate surrogate models, using fewer datapoints accelerates the overall adjustment process and might allow to selectively choose the subsequent datapoints based on the model's prediction instead of initial random allocation. Within this case study we aim to identify this effect by comparing a GP, initialized with two, three and four random datapoints.