Fitness function evaluates the quality of each chromosome. It is probably the most important component in genetic algorithm, because it directly defines the performance of each chromosome and intimately characterizes the ideal solution that the user attempts to search for. Because the intension of the proposed soft computing model is to produce highly interpretable rules without sacrificing accuracy, fitness function f is defined in Eq. (5), where performance evaluations of both accuracy and interpretability are incorporated. We use capital letters to represent constants and small letters to represent variables. Term-1 and term-5 in Eq. (5) represent the accuracy of the model and the rest three terms represent the interpretability of the model because they are the scores on the number of selected features, the number of derived fuzzy rules, and the total number of arguments in the antecedent part of all rules, respectively. This fitness function is to be minimized by the genetic algorithm.