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