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

► 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...

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Published inExpert systems with applications Vol. 40; no. 8; pp. 3282 - 3298
Main Author Tsakonas, Athanasios
Format Journal Article
LanguageEnglish
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
Online AccessGet full text
ISSN0957-4174
1873-6793
DOI10.1016/j.eswa.2012.12.099

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Abstract ► 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.
AbstractList 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.
► 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.
Author Tsakonas, Athanasios
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crossref_primary_10_1007_s12293_016_0220_3
crossref_primary_10_1016_j_eswa_2013_08_038
crossref_primary_10_3103_S8756699017040100
crossref_primary_10_1016_j_asoc_2015_08_055
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Issue 8
Keywords 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
Language English
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SSID ssj0017007
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Snippet ► We propose a method to incorporate local search in GP-evolved intelligent structures. ► We combine neuro-fuzzy and fuzzy-evolutionary training for...
This work presents a method to incorporate standard neuro-fuzzy learning for Takagi-Sugeno fuzzy systems that evolve under a grammar driven genetic programming...
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StartPage 3282
SubjectTerms 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
Title Local and global optimization for Takagi–Sugeno fuzzy system by memetic genetic programming
URI https://dx.doi.org/10.1016/j.eswa.2012.12.099
https://www.proquest.com/docview/1671442076
Volume 40
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