Learning internal representations by error propagation
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Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Neuro-fuzzy systems for function approximation
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Linear Prediction Theory: A Mathematical Basis for Adaptive Systems
Linear Prediction Theory: A Mathematical Basis for Adaptive Systems
Genetic programming for model selection of TSK-fuzzy systems
Information Sciences: an International Journal - Recent advances in genetic fuzzy systems
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Genetic Programming and Evolvable Machines
Evolutionary algorithms with local search for combinatorial optimization
Evolutionary algorithms with local search for combinatorial optimization
Applying online gradient descent search to genetic programming for object recognition
ACSW Frontiers '04 Proceedings of the second workshop on Australasian information security, Data Mining and Web Intelligence, and Software Internationalisation - Volume 32
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A probabilistic memetic framework
IEEE Transactions on Evolutionary Computation
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A comparison of classification accuracy of four genetic programming-evolved intelligent structures
Information Sciences: an International Journal
Meta-learning genetic programming
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A tutorial for competent memetic algorithms: model, taxonomy, and design issues
IEEE Transactions on Evolutionary Computation
An approach to online identification of Takagi-Sugeno fuzzy models
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Automatic Design of Hierarchical Takagi–Sugeno Type Fuzzy Systems Using Evolutionary Algorithms
IEEE Transactions on Fuzzy Systems
Self-learning fuzzy controllers based on temporal backpropagation
IEEE Transactions on Neural Networks
Expert Systems with Applications: An International Journal
Performance evaluation of microbial fuel cell by artificial intelligence methods
Expert Systems with Applications: An International Journal
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This work presents a method to incorporate standard neuro-fuzzy learning for Takagi-Sugeno fuzzy systems that evolve under a grammar driven genetic programming (GP) framework. This is made possible by introducing heteroglossia in the functional GP nodes, enabling them to switch behavior according to the selected learning stage. A context-free grammar supports the expression of arbitrarily sized and composed fuzzy systems and guides the evolution. Recursive least squares and backpropagation gradient descent algorithms are used as local search methods. A second generation memetic approach combines the genetic programming with the local search procedures. Based on our experimental results, a discussion is included regarding the competitiveness of the proposed methodology and its properties. The contributions of the paper are: (i) introduction of an approach which enables the application of local search learning for intelligent systems evolved by genetic programming, (ii) presentation of a model for memetic learning of Takagi-Sugeno fuzzy systems, (iii) experimental results evaluating model variants and comparison with state-of-the-art models in benchmarking and real-world problems, (iv) application of the proposed model in control.