Genetic Learning of Fuzzy Parameters in Predictive and Decision Support Modelling
Àngela Nebot, Francisco Mugica, Félix Castro, Jesús Alejandro Navarro Acosta · International Journal of Computational Intelligence Systems · 2012
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.