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

Created by W.Langdon from gp-bibliography.bib Revision:1.4192

  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

Genetic Programming entries for Athanasios D Tsakonas