Applying genetics to fuzzy logic
AI Expert
Outline for a Logical Theory of Adaptive Systems
Journal of the ACM (JACM)
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
Genetic Algorithms and Machine Learning
Machine Learning
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
Zero-order TSK-type fuzzy system learning using a two-phase swarm intelligence algorithm
Fuzzy Sets and Systems
Design of Mamdani fuzzy logic controllers with rule base minimisation using genetic algorithm
Engineering Applications of Artificial Intelligence
Designing fuzzy-rule-based systems using continuous ant-colony optimization
IEEE Transactions on Fuzzy Systems
Complex systems modeling via fuzzy logic
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A hybrid of genetic algorithm and particle swarm optimization for recurrent network design
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Self-organized fuzzy system generation from training examples
IEEE Transactions on Fuzzy Systems
Evolutionary design of fuzzy rule base for nonlinear system modeling and control
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
Stable adaptive fuzzy control of nonlinear systems
IEEE Transactions on Fuzzy Systems
Identification and control of dynamical systems using neural networks
IEEE Transactions on Neural Networks
International Journal of Hybrid Intelligent Systems
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In this paper, Hybrid Elite Genetic Algorithm and Tabu Search HEGATS is proposed to automatically generate Fuzzy rule base for fuzzy inference systems. The algorithm is used to simultaneously optimize the premise and consequent parameters of the fuzzy rules for the appropriate design of fuzzy system for Takagi-Sugeno zero-order. After finale selection of the new generation calculated by genetic algorithm, elitist solution is saved. In this step, tabu search is introduced to find the better neighboring of the elitist solution which will be introduced in the new generation. This hybridization of global and local optimization algorithms minimizes the number of iterations and computation time while maintaining accuracy and minimum response time. To demonstrate the effectiveness of the proposed algorithm, several numerical examples given in the literature for control and modeling systems are examined. Results prove the effectiveness of the proposed algorithm.