of genetic algorithms (GAs) or genetic programming (GP). Genetic algorithms for fuzzy system identification Given the high degree of nonlinearity of the output of a fuzzy system, traditional linear optimization tools do have their limitations. Genetic algorithms have demonstrated to be a robust and very powerful tool to perform tasks such as the generation of fuzzy rule base, optimization of fuzzy rule bases, generation of membership functions, and tuning of membership functions (Cordón et al., 2001a). All these tasks can be considered as optimization or search processes within large solution spaces (Bastian and Hayashi, 1995) (Yuan and Zhuang, 1996) (Cordón et al., 2001b). Genetic programming for fuzzy system identification While genetic algorithms are very powerful tools to identify the fuzzy membership functions of a pre-defined rule base, they have their limitation especially when it also comes to identify the input and output variables of a fuzzy system from a given set of data. Genetic programming has been used to identify the input variables, the rule base as well as the involved membership functions of a fuzzy model (Bastian, 2000) Multiobjective Genetic Fuzzy Systems In the last decade multi-objective optimization of fuzzy rule based systems has attracted wide interest within the research community and practitioners. It is based on the use of stochastic algorithms for Multi-objective optimization to search for the Pareto efficiency in a multiple objectives scenario. For instance, the objectives to simultaneously optimize can be accuracy and complexity, or accuracy and interpretability. A recent review of the field is provided in the work of Fazzolari et al. (2013). In addition, [1] provides an up-to-date and continuously growing list of references on the subject. References 1974, E.H. Mamdani, Applications of fuzzy algorithms for control of simple dynamic plant, Proc. IEE 121 1584 - 1588. 1995, A. Bastian, I. Hayashi: "An Anticipating Hybrid Genetic Algorithm for Fuzzy Modeling", Journal of Japan Society for Fuzzy Theory and Systems, Vol.10, pp. 801–810 1995, Klir, G. B. Yuan, Fuzzy sets and Fuzzy Logic - Theory and Applications, Prentice-Hall. 1996, Y. Yuan and H. Zhuang, "A genetic algorithm for generating fuzzy classification rules", Fuzzy Sets and Systems, V. 84, N. 4, pp. 1–19. 1998, W. Pedrycz and F. Gomide, An Introduction to Fuzzy Sets: Analysis and Design, MIT Press. 2000, A. Bastian: ”Identifying Fuzzy Models utilizing Genetic Programming”, Fuzzy Sets and Systems 113, 333–350. 2001, O. Cordón, F. Herrera, F. Gomide, F. Hoffmann and L.