Nebot, A., Mugica, F., Castro, F., Acosta, J. (2012). Genetic Learning of Fuzzy Parameters in Predictive and Decision Support Modelling. International Journal of Computational Intelligence Systems, 5:2, Pp. 387-402.
In this research a genetic fuzzy system (GFS) is proposed that performs discretization parameter learning in the context of the Fuzzy Inductive Reasoning (FIR) methodology and the Linguistic Rule FIR (LR-FIR) algorithm. The main goal of the GFS is to take advantage of the potentialities of GAs to learn the fuzzification parameters of the FIR and LR-FIR approaches in order to obtain reliable and useful predictive (FIR) models and decision support (LR-FIR) models. The GFS is evaluated in an e-learning context.