Data-driven system identification via evolutionary retrieval of Takagi-Sugeno fuzzy models

System identification is the task to map several related components of a real world system into a model. If this is done by transferring human expertise into a model, the process is called knowledge-driven modeling. If the system information is embedded in data-bases and the implicit existent expertise is mapped by algorithms into a model, the process is called data-driven modeling. This thesis suggests for data-driven system identification the class of TakagiSugeno fuzzy models as target. This class of models provides the possibility to make use of powerful learning algorithms. On the other hand the human interpretability of the resulting models can be assured. Because of this, necessary interpretability factors are worked out and an objective interpretability measure for Takagi-Sugeno fuzzy models is formulated. Evolutionary computation, as a general search method, is used to identify an optimal model structure. Optimal and sparse model structures are desirable for reasons of accuracy and generalization capability. The way in which candidate solutions (i.e. models), are encoded in evolutionary algorithms is a central factor in population based search methods. The author proposes a novel grammar based method to formulate genotype-templates. These templates will be used to define the genotype search space. The presented approach of data-driven system identification via evolutionary retrieval of Takagi-Sugeno fuzzy models is tested with artificial data and with a complex real world dataset considering the prediction of molecular toxicity.