1 May 2003 Optimization of a fuzzy classification by evolutionary strategies
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Proceedings Volume 5132, Sixth International Conference on Quality Control by Artificial Vision; (2003); doi: 10.1117/12.514965
Event: Quality Control by Artificial Vision, 2003, Gatlinburg, TE, United States
Abstract
The fuzzy C-means algorithm is an unsupervised classification algorithm. This algorithm however, suffers from two difficulties which are the initialization phase and the local optimums. We present in this paper some improvements to this algorithm based on the evolutionary strategies in order to get around these two difficulties. We have designed a new evolutionist fuzzy C-means algorithm. We have proposed a new mutation operator in order for the algorithm to avoid local solutions and to converge to the global solution for a low computational time. This approach is validated on some simulation examples. The experimental results obtained confirm the rapidity of convergence and the good performances of the proposed algorithm.
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M. Nasri, M. El Hitmy, H. Ouariachi, M. Barboucha, "Optimization of a fuzzy classification by evolutionary strategies", Proc. SPIE 5132, Sixth International Conference on Quality Control by Artificial Vision, (1 May 2003); doi: 10.1117/12.514965; https://doi.org/10.1117/12.514965
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KEYWORDS
Fuzzy logic

Chemical elements

Genetic algorithms

Genetics

Radon

Clouds

Electroluminescence

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