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Applied Computational Intelligence and Soft Computing
Volume 2011 (2011), Article ID 938240, 20 pages
Review Article

Evolutionary Computation and Its Applications in Neural and Fuzzy Systems

1Central Research Institute, Enjoyor Inc., Hangzhou 310030, China
2Faculty of Electromechanical Engineering, Guangdong University of Technology, Guangzhou 510006, China
3Department of Electrical and Computer Engineering, Concordia University, Montreal, QC, Canada H3G 1M8

Received 7 March 2011; Revised 6 July 2011; Accepted 4 August 2011

Academic Editor: Miin-Shen Yang

Copyright © 2011 Biaobiao Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.