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Genetic identification of models of dynamical systems is becoming a well stablished research field. Nowadays it is hard to obtain more precise numerical results than state of the art methods, but, in our oppinion, there is still room to improve the understandability of genetically induced models. In this paper it is proposed a method that focuses in the comprehensibility of the final model, while keeping most of the numerical precision of former studies. The main innovation in this work is centered in the concept of \understandable" system. We do not use state space designed, rule based models, but z-transform based models, comprising linear, discrete dynamical models of first or second order and memoriless nonlinear elements (saturation, dead zone or other nonlinear gains.) This way, we provide control engineers with their prefered representation in moderate to complex models, and facilitate the task of designing control systems for these processes.