It is commonly established that more intelligent systems can be obtained by the hybridization of Soft Computing methodologies, in order that the weaknesses of some systems be compensated with the strengths of others. Neural Fuzzy Systems (NFSs) and Evolutionary Fuzzy Systems (EFSs) are the most notorious representatives of these hybrid systems. An Evolutionary Fuzzy System is basically a fuzzy system augmented by a learning process based on an evolutionary algorithm (EA), particularly Genetic Algorithms (GAs), which are currently considered as the most well-known employed global search technique. This kind of algorithms have the ability to explore and to exploit complex search spaces, which allows the obtaining of solutions very close to the optimal ones within these spaces. Besides, the genetic codification employed allows to incorporate a priori knowledge in a very simple way and to use it to guide the search. In this PhD. thesis, we propose EFSs that improves a modeling and simulation technique the Fuzzy Inductive Reasoning (FIR). The main goal of the EFSs is to take advantage of the potentialities of GAs to learn the fuzzification parameters of FIR, i.e. the number of classes per variable (granularity) and the membership functions (landmarks) that define its semantics. Due to the fact that it is a methodology based on fuzzy logic, FIR modeling and prediction performance is directly influenced by these discretization parameters. Therefore, the automatic determination of precise fuzzification parameters in the FIR methodology is an interesting and useful alternative to the use of heuristics and/or default values. Moreover, it is expected that the automatic selection of adequate values for these parameters will open up the FIR methodology to new users, with no experience neither in systems modeling nor in fuzzy logic, guaranteeing the best performance of this methodology. Three evolutionary methods of automatic learning of fuzzy partitions are presented: a) The learning of the granularity with uniform membership functions (GA1+EFP), b) The local tuning of the membership functions with a fixed number of classes per variable (GA1+GA2), and c) The learning at the same time of the granularity and the membership functions associated that define its semantics (GA3). The evolutionary methods have been implemented in Matlab and they run in both Windows and Linux environments. The results obtained by the EFSs developed in the four applications studied, i.e. human central nervous system, maintenance costs of electrical medium line in Spanish towns, short-term estimation of ozone concentration in Austria and long-term estimation of ozone concentration in Mexico, were very good. The results obtained by our evolutionary methods have presented higher efficiency in the prediction process than those obtained by other methodologies in previous works, by FIR using default values and, even, by FIR when the fuzzification parameters have been defined by experts in the area. In general, the GA3 and the combination GA1+GA2, in that order, are the ones that have shown better results in all the applications, followed by the GA1+EFP. However, GA3 is the algorithm that presents the greatest computational cost. As general conclusion, we must say that the EFSs designed and implemented in this thesis yielded good results for the task which they were entrusted in FIR methodology. Therefore, the user should decide what EFS turns out to be more convenient for the modeling application at hand in function of time and precision needs.