We hypothesized that using a combination of active surrogate models within the same optimization strategy may increase performance compared to single models. In detail, we based our investigation on the fact that the current best model (ANN) typically does not perform well at the beginning of the optimization, when very little data is available. Random forest, however, seems to perform better under a low data regime. Within the RF-ANN hybrid model, the rule was set that during the first 10 iterations the decision-making was conducted based on the predictions of the RF and then the ANN continued. Unexpectedly, the performance of the hybrid model did not outperform ANN. Nonetheless, it was the secondbest model and achieved the optimal conditions within 70 iterations.