Based on our previous work , we used a hybrid approach of heuristic optimization and nonlinear as an additional pre-processing step before the training . This algorithm performs boosting, assigning weights over the internal training set in the cross-validation iterations. This algorithm assigns higher weights to the minority class and then generates synthetic copies of the minority class samples until all the classes are balanced. This is especially helpful for the unbalanced class, i.e., the umami one. The outcome is the generation of multiple models that exhibit similar performance concerning the userdefined objectives, which correspond to the Pareto set of optimal solutions.