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

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@Article{Tsakonas:2013:ESA,

• author = "Athanasios Tsakonas",
• title = "Local and global optimization for Takagi-Sugeno fuzzy system by memetic genetic programming",
• journal = "Expert Systems with Applications",
• volume = "40",
• number = "8",
• pages = "3282--3298",
• year = "2013",
• keywords = "genetic algorithms, genetic programming, Neuro-fuzzy systems, Context-free grammars, Evolutionary computation, Recursive least squares",
• ISSN = "0957-4174",
• DOI = "doi:10.1016/j.eswa.2012.12.099",
• URL = "http://www.sciencedirect.com/science/article/pii/S0957417412013413",
• abstract = "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 behaviour 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.",

}

Genetic Programming entries for Athanasios D Tsakonas

Citations