This paper presents a method for identifying systems through their input-output behavior and the Genetic Algorithm (GA). The advantages of this technique are, first, it is not dependent on the deterministic or stochastic nature of the systems and, second, the globally optimized models for the original systems can be identified without the need of a differentiable measure function or linearly separable parameters. The results are compared to similar results from Least Squares (LS) identification methods.