Local Identification of Prototypes for Genetic Learning of Accurate TSK Fuzzy Rule-Based Systems Alcalá Fernández, Rafael Alcalá Fernández, Jesús Casillas Barranquero, Jorge Cordón García, Óscar Herrera Triguero, Francisco This work presents the use of local fuzzy prototypes as a new idea to obtain accurate local semantics-based Takagi–Sugeno–Kang ~TSK! rules. This allow us to start from prototypes considering the interaction between input and output variables and taking into account the fuzzy nature of the TSK rules. To do so, a two-stage evolutionary algorithm based on MOGUL ~a methodology to obtain Genetic Fuzzy Rule-Based Systems under the Iterative Rule Learning approach! has been developed to consider the interaction between input and output variables. The first stage performs a local identification of prototypes to obtain a set of initial local semantics-based TSK rules, following the Iterative Rule Learning approach and based on an evolutionary generation process within MOGUL ~taking as a base some initial linguistic fuzzy partitions!. Because this generation method induces competition among the fuzzy rules, a postprocessing stage to improve the global system performance is needed. Two different processes are considered at this stage, a genetic niching-based selection process to remove redundant rules and a genetic tuning process to refine the fuzzy model parameters. The proposal has been tested with two real-world problems, achieving good results. 2020-12-17T08:15:41Z 2020-12-17T08:15:41Z 2007 info:eu-repo/semantics/article Alcala, R., Alcala-Fdez, J., Casillas, J., Cordon, O., & Herrera, F. (2007). Local identification of prototypes for genetic learning of accurate TSK fuzzy rule-based systems. International Journal of Intelligent Systems, 22(9), 909-941. doi:10.1002/int.20232 http://hdl.handle.net/10481/64967 10.1002/int.20232 eng http://creativecommons.org/licenses/by/3.0/es/ info:eu-repo/semantics/openAccess Atribución 3.0 España WILEY