Optimization of control parameters for genetic algorithms
IEEE Transactions on Systems, Man and Cybernetics
Reducing bias and inefficiency in the selection algorithm
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
An introduction to fuzzy control
An introduction to fuzzy control
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
A genetic-algorithm-based method for tuning fuzzy logic controllers
Fuzzy Sets and Systems
A GA-based fuzzy adaptive learning control network
Fuzzy Sets and Systems
Fuzzy Modelling: Paradigms and Practices
Fuzzy Modelling: Paradigms and Practices
Information Sciences: an International Journal - Recent advances in genetic fuzzy systems
Industrial Applications of Fuzzy Technology in the World
Industrial Applications of Fuzzy Technology in the World
Fuzzy Algorithms: With Applications to Image Processing and Pattern Recognition
Fuzzy Algorithms: With Applications to Image Processing and Pattern Recognition
Simulation Modeling and Analysis
Simulation Modeling and Analysis
Fuzzy Theory Systems: Techniques and Applications
Fuzzy Theory Systems: Techniques and Applications
The GA-P: A Genetic Algorithm and Genetic Programming Hybrid
IEEE Expert: Intelligent Systems and Their Applications
IEA/AIE '98 Proceedings of the 11th international conference on Industrial and engineering applications of artificial intelligence and expert systems: methodology and tools in knowledge-based systems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Document retrieval using fuzzy-valued concept networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
FuGeNeSys-a fuzzy genetic neural system for fuzzy modeling
IEEE Transactions on Fuzzy Systems
Evolution-based design of neural fuzzy networks using self-adapting genetic parameters
IEEE Transactions on Fuzzy Systems
Online global learning in direct fuzzy controllers
IEEE Transactions on Fuzzy Systems
Induction of fuzzy-rule-based classifiers with evolutionary boosting algorithms
IEEE Transactions on Fuzzy Systems
Strategies to identify fuzzy rules directly from certainty degrees: a comparison and a proposal
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
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Fuzzy Inductive Reasoning (FIR) is a data-driven methodology that uses fuzzy and pattern recognition techniques to infer system models and to predict their future behavior. It is well known that variations on fuzzy partitions have a direct effect on the performance of the fuzzy-rule-based systems. The FIR methodology is not an exception. The performance of the model identification and prediction processes of FIR is highly influenced by the discretization parameters of the system variables, i.e. the number of classes of each variable and the membership functions that define its semantics. In this work, we design two new genetic fuzzy systems (GFSs) that improve this modeling and simulation technique. The main goal of the GFSs is to learn the fuzzification parameters of the FIR methodology. The new approaches are applied to two real modeling problems, the human central nervous system and an electrical distribution problem.