Local and global optimization for Takagi–Sugeno fuzzy system by memetic genetic programming

• Published in: Expert systems with applications Vol. 40; no. 8; pp. 3282 - 3298
• Main Author: Tsakonas, Athanasios
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier Ltd 15.06.2013; Elsevier
• Subjects: Algorithmics. Computability. Computer arithmetics; Algorithms; Applied sciences; Artificial intelligence; Computer science; control theory; systems; Connectionism. Neural networks; Context-free grammars; Evolutionary computation; Exact sciences and technology; Firm modelling; Fuzzy systems; Genetics; Grammars; Learning; Learning and adaptive systems; Memetic genetic programming; Neuro-fuzzy systems; Operational research and scientific management; Operational research. Management science; Programming; Recursive least squares; Search methods; Searching; Theoretical computing; Memetic genetic programming; Neuro-fuzzy systems; Recursive least squares; Context-free grammars; Evolutionary computation; Performance evaluation; Software maintenance; Search system; Evolutionary algorithm; Competitiveness; System programming; Modeling; Learning systems; Gradient descent; Memetic algorithm; Backpropagation algorithm; Least squares method; Intelligent system; Local search; Gradient; Fuzzy system; Adaptive resonance theory; Local optimum; Benchmarking; Global optimum; Neural network; Standards; Search algorithm; Global local method; Fuzzy logic; Experimental result; Genetic algorithm; Descent method; Context free grammar; Recursive method; Artificial intelligence
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2012.12.099

► We propose a method to incorporate local search in GP-evolved intelligent structures. ► We combine neuro-fuzzy and fuzzy-evolutionary training for Takagi–Sugeno fuzzy systems. ► We apply the proposed system to regression, forecasting and control problems. 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.