Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
International Journal of Approximate Reasoning
A Pareto-based multi-objective evolutionary approach to the identification of Mamdani fuzzy systems
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Interpretability constraints for fuzzy information granulation
Information Sciences: an International Journal
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Genetic Fuzzy Systems: Recent Developments and Future Directions; Guest editors: Jorge Casillas, Brian Carse
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Genetic Fuzzy Systems: Recent Developments and Future Directions; Guest editors: Jorge Casillas, Brian Carse
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Genetic Fuzzy Systems: Recent Developments and Future Directions; Guest editors: Jorge Casillas, Brian Carse
International Journal of Approximate Reasoning
Looking for a good fuzzy system interpretability index: An experimental approach
International Journal of Approximate Reasoning
IEEE Transactions on Fuzzy Systems
Multi-objective genetic fuzzy classifiers for imbalanced and cost-sensitive datasets
Soft Computing - A Fusion of Foundations, Methodologies and Applications
A dynamically constrained multiobjective genetic fuzzy system for regression problems
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems - Special section on computing with words
Interpretability of linguistic fuzzy rule-based systems: An overview of interpretability measures
Information Sciences: an International Journal
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on Evolutionary Fuzzy Systems
Semantic constraints for membership function optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Designing fuzzy inference systems from data: An interpretability-oriented review
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
An efficient multi-objective evolutionary fuzzy system for regression problems
International Journal of Approximate Reasoning
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Several methods have been proposed to automatically generate fuzzy rule-based systems (FRBSs) from data. At the beginning, the unique objective of these methods was to maximize the accuracy with the result of often neglecting the most distinctive feature of the FRBSs, namely their interpretability. Thus, in the last years, the automatic generation of FRBSs from data has been handled as a multiobjective optimization problem, with accuracy and interpretability as objectives. Multi-objective evolutionary algorithms (MOEAs) have been so often used in this context that the FRBSs generated by exploiting MOEAs have been denoted as multi-objective evolutionary fuzzy systems. In this paper, we introduce a taxonomy of the different approaches which have been proposed in this framework. For each node of the taxonomy, we describe the relevant works pointing out the most interesting features. Finally, we highlight current trends and future directions.