Applying genetics to fuzzy logic
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Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
A genetic algorithm for optimizing Takagi-Sugeno fuzzy rule bases
Fuzzy Sets and Systems
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms and Soft Computing
Genetic Algorithms and Soft Computing
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
Tuning of a neuro-fuzzy controller by genetic algorithm
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Forecasting time series with genetic fuzzy predictor ensemble
IEEE Transactions on Fuzzy Systems
Self-organized fuzzy system generation from training examples
IEEE Transactions on Fuzzy Systems
GA-fuzzy modeling and classification: complexity and performance
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
Comparison of adaptive methods for function estimation from samples
IEEE Transactions on Neural Networks
Cooperative Coevolution for Learning Fuzzy Rule-Based Systems
Selected Papers from the 5th European Conference on Artificial Evolution
Automatic adaptive modeling of fuzzy systems using particle swarm optimization
Transactions on computational science VIII
Automatic adaptive modeling of fuzzy systems using particle swarm optimization
Transactions on computational science VIII
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The design of fuzzy logic systems (FLS) generally involves determining the structure of the rules and the parameters of the membership functions. In this paper we present a methodology based on evolutionary computation for simultaneously designing membership functions and appropriate rule sets. This property makes it different from many techniques that address these goals separately with the result of suboptimal solutions because the design elements are mutually dependent. We also apply a new approach in which the evolutionary algorithm is applied directly to a FLS data structure instead of a binary or other codification. Results on function approximation show improvements over other incremental and analytical methods.