MATLAB Source Code for Fuzzy C-Means Clustering Improved by Genetic Algorithm

The Fuzzy C-Means algorithm often converges to local minima, limiting its optimization performance. To address this issue, we apply genetic algorithms to enhance the FCM optimization process. The implementation first utilizes genetic algorithms to obtain initial cluster centers, followed by standard FCM clustering to achieve optimal classification results. The code includes genetic operations such as selection, crossover, and mutation applied to cluster center initialization.

To further explore the application of genetic algorithms in fuzzy C-means clustering, we developed a comprehensive MATLAB source code that combines the advantages of both techniques. The implementation features population initialization with random cluster centers, fitness calculation based on FCM objective functions, and elitism selection to preserve optimal solutions. The genetic algorithm phase prevents premature convergence by maintaining population diversity.

The enhanced fuzzy C-means clustering with genetic algorithm integration enables more accurate data classification and cluster analysis. This approach not only overcomes the local minima problem inherent in standard FCM but also improves clustering accuracy and stability. The algorithm includes customizable parameters for population size, mutation rate, and termination criteria to accommodate different dataset characteristics.

This MATLAB source code provides researchers and engineers with a powerful tool for applying genetic algorithms to fuzzy C-means clustering, yielding superior classification outcomes. Key functions include GA_initialization for population generation, FCM_evaluation for fitness calculation, and cluster_optimization for final refinement. We believe this implementation will find extensive applications in data mining and pattern recognition domains, particularly for complex datasets requiring robust clustering solutions.