Automatic Optimal Design of Fuzzy Systems Based on Universal Approximation and Evolutionary Programming

Nyberg, Mattias; Pao, Yoh‐Han

doi:10.1007/978-0-585-28000-4_12

Cited by 7 publications

(1 citation statement)

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“…Specific genetic operators such as rule insertion and rule deletion, where a whole rule is added or deleted at a specified point of the string, are also applied [144]. In [145], automatic optimal design of fuzzy systems is conducted by using the GESA [120].…”

Section: Constructing Fuzzy Systems Using Evolutionary Algorithmsmentioning

confidence: 99%

Evolutionary Computation and Its Applications in Neural and Fuzzy Systems

Zhang¹,

Wu²,

et al. 2011

Applied Computational Intelligence and Soft Computing

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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 neoDarwinian 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.

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Section: Constructing Fuzzy Systems Using Evolutionary Algorithmsmentioning

confidence: 99%