Evolutionary Computation and Its Applications in Neural and Fuzzy Systems
Neural networks and fuzzy systems are two soft-computing paradigms for system modelling. Adapting a neural or fuzzy system requires to solve two optimization problems: structural optimization and parametric optimization. Structural optimization is a discrete optimization problem which is very hard t...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2011-01-01
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| Series: | Applied Computational Intelligence and Soft Computing |
| Online Access: | http://dx.doi.org/10.1155/2011/938240 |
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| _version_ | 1832556106604347392 |
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| author | Biaobiao Zhang Yue Wu Jiabin Lu K.-L. Du |
| author_facet | Biaobiao Zhang Yue Wu Jiabin Lu K.-L. Du |
| author_sort | Biaobiao Zhang |
| collection | DOAJ |
| description | Neural networks and fuzzy systems are two soft-computing paradigms for system modelling. Adapting a neural or fuzzy system requires to solve two optimization problems: structural optimization and parametric optimization. Structural optimization is a discrete optimization problem which is very hard to solve using conventional optimization techniques. Parametric optimization can be solved using conventional optimization techniques, but the solution may be easily trapped at a bad local optimum. Evolutionary computation is a general-purpose stochastic global optimization approach under the universally accepted neo-Darwinian paradigm, which is a combination of the classical Darwinian evolutionary theory, the selectionism of Weismann, and the genetics of Mendel. Evolutionary algorithms are a major approach to adaptation and optimization. In this paper, we first introduce evolutionary algorithms with emphasis on genetic algorithms and evolutionary strategies. Other evolutionary algorithms such as genetic programming, evolutionary programming, particle swarm optimization, immune algorithm, and ant colony optimization are also described. Some topics pertaining to evolutionary algorithms are also discussed, and a comparison between evolutionary algorithms and simulated annealing is made. Finally, the application of EAs to the learning of neural networks as well as to the structural and parametric adaptations of fuzzy systems is also detailed. |
| format | Article |
| id | doaj-art-1ecd0ad040594d7f8a31ce350079f2be |
| institution | Kabale University |
| issn | 1687-9724 1687-9732 |
| language | English |
| publishDate | 2011-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Applied Computational Intelligence and Soft Computing |
| spelling | doaj-art-1ecd0ad040594d7f8a31ce350079f2be2025-02-03T05:46:19ZengWileyApplied Computational Intelligence and Soft Computing1687-97241687-97322011-01-01201110.1155/2011/938240938240Evolutionary Computation and Its Applications in Neural and Fuzzy SystemsBiaobiao Zhang0Yue Wu1Jiabin Lu2K.-L. Du3Central Research Institute, Enjoyor Inc., Hangzhou 310030, ChinaCentral Research Institute, Enjoyor Inc., Hangzhou 310030, ChinaFaculty of Electromechanical Engineering, Guangdong University of Technology, Guangzhou 510006, ChinaCentral Research Institute, Enjoyor Inc., Hangzhou 310030, ChinaNeural networks and fuzzy systems are two soft-computing paradigms for system modelling. Adapting a neural or fuzzy system requires to solve two optimization problems: structural optimization and parametric optimization. Structural optimization is a discrete optimization problem which is very hard to solve using conventional optimization techniques. Parametric optimization can be solved using conventional optimization techniques, but the solution may be easily trapped at a bad local optimum. Evolutionary computation is a general-purpose stochastic global optimization approach under the universally accepted neo-Darwinian paradigm, which is a combination of the classical Darwinian evolutionary theory, the selectionism of Weismann, and the genetics of Mendel. Evolutionary algorithms are a major approach to adaptation and optimization. In this paper, we first introduce evolutionary algorithms with emphasis on genetic algorithms and evolutionary strategies. Other evolutionary algorithms such as genetic programming, evolutionary programming, particle swarm optimization, immune algorithm, and ant colony optimization are also described. Some topics pertaining to evolutionary algorithms are also discussed, and a comparison between evolutionary algorithms and simulated annealing is made. Finally, the application of EAs to the learning of neural networks as well as to the structural and parametric adaptations of fuzzy systems is also detailed.http://dx.doi.org/10.1155/2011/938240 |
| spellingShingle | Biaobiao Zhang Yue Wu Jiabin Lu K.-L. Du Evolutionary Computation and Its Applications in Neural and Fuzzy Systems Applied Computational Intelligence and Soft Computing |
| title | Evolutionary Computation and Its Applications in Neural and Fuzzy Systems |
| title_full | Evolutionary Computation and Its Applications in Neural and Fuzzy Systems |
| title_fullStr | Evolutionary Computation and Its Applications in Neural and Fuzzy Systems |
| title_full_unstemmed | Evolutionary Computation and Its Applications in Neural and Fuzzy Systems |
| title_short | Evolutionary Computation and Its Applications in Neural and Fuzzy Systems |
| title_sort | evolutionary computation and its applications in neural and fuzzy systems |
| url | http://dx.doi.org/10.1155/2011/938240 |
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