遗传算法 Resources

Showing items tagged with "遗传算法"

The Fuzzy C-Means (FCM) algorithm tends to converge to local minima. To overcome this limitation, we integrate genetic algorithms into the FCM optimization process. The genetic algorithm generates initial cluster centers, which are then refined using the standard FCM algorithm to achieve optimal classification results. This implementation features chromosome encoding for cluster centers, fitness evaluation using FCM objective functions, and iterative population evolution.

MATLAB 229 views Tagged
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Implementation of genetic algorithm-optimized neural networks for time series prediction. The genetic.m interface function provides straightforward configuration, allowing direct modification of neural network parameters. Users can easily substitute their own data files by updating the load function call, enabling efficient adaptation to diverse datasets.

MATLAB 194 views Tagged
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This approach combines traditional Otsu's thresholding method with modern genetic algorithm optimization to enhance image segmentation performance. When applied to road segmentation in intelligent transportation systems, it achieves improved segmentation accuracy while significantly accelerating computational efficiency to meet real-time processing requirements. The implementation involves optimizing threshold selection through genetic operators like crossover and mutation.

MATLAB 191 views Tagged
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