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Genetic Learning of the Knowledge Base of a Fuzzy System by Using the Linguistic 2-Tuples Representation.

, , , and . FUZZ-IEEE, page 797-802. IEEE, (2005)

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On the Usefulness of MOEAs for Getting Compact FRBSs Under Parameter Tuning and Rule Selection., , , and . Multi-Objective Evolutionary Algorithms for Knowledge Discovery from Databases, volume 98 of Studies in Computational Intelligence, Springer, (2008)A Multiobjective Evolutionary Algorithm for Tuning Fuzzy Rule Based Systems with Measures for Preserving Interpretability., , and . IFSA/EUSFLAT Conf., page 1146-1151. (2009)Linguistic modeling with weighted double-consequent fuzzy rules based on cooperative coevolution., , , and . EUSFLAT Conf., page 237-240. De Montfort University, Leicester, UK, (2001)A case study on the application of instance selection techniques for Genetic Fuzzy Rule-Based Classifiers., , , , and . FUZZ-IEEE, page 1-8. IEEE, (2012)Genetic tuning on fuzzy systems based on the linguistic 2-tuples representation., and . FUZZ-IEEE, page 233-238. IEEE, (2004)Obtaining accurate TSK Fuzzy Rule-Based Systems by Multi-Objective Evolutionary Learning in high-dimensional regression problems., , , and . FUZZ-IEEE, page 1-7. IEEE, (2013)Learning positive-negative rule-based fuzzy associative classifiers with a good trade-off between complexity and accuracy., , , , , and . Fuzzy Sets Syst., (August 2023)Fuzzy Control of HVAC Systems Optimized by Genetic Algorithms., , , , and . Appl. Intell., 18 (2): 155-177 (2003)A Double Axis Classification of Interpretability Measures for Linguistic Fuzzy Rule-Based Systems., , and . WILF, volume 6857 of Lecture Notes in Computer Science, page 99-106. Springer, (2011)Handling High-Dimensional Regression Problems by Means of an Efficient Multi-Objective Evolutionary Algorithm., , and . ISDA, page 109-114. IEEE Computer Society, (2009)