SVM with Genetic Algorithm Optimization for C and G Parameters
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In this project, we implemented a MATLAB-based Support Vector Machine (SVM) for classification tasks. To enhance model performance, we integrated a genetic algorithm to optimize two critical parameters: C (penalty factor) and G (kernel parameter, typically gamma in RBF kernel). The genetic algorithm implementation employs selection, crossover, and mutation operators to evolve parameter combinations across generations. Through iterative population evolution and fitness evaluation (using classification accuracy as the objective function), the algorithm efficiently explores the parameter space to identify optimal C and G values. This optimization approach enables the SVM model to achieve superior classification performance by automatically balancing model complexity and generalization capability. Key MATLAB functions involved include ga for genetic algorithm optimization and fitcsvm for SVM model training with customized kernel parameters.
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