Genetic Algorithm Optimization for Support Vector Machine Algorithm

The document addresses a key limitation where standalone Support Vector Machine (SVM) implementations demonstrate insufficient recognition accuracy. To overcome this challenge, our program integrates Genetic Algorithms (GA) with SVM to optimize its performance parameters. Genetic Algorithms simulate natural selection and genetic mechanisms to efficiently explore optimal solutions in complex parameter spaces. The implementation involves using GA to systematically adjust SVM's hyperparameters (such as kernel parameters and penalty factors) and feature weights, enabling better adaptation to diverse datasets and problem domains. Through fitness-based chromosome evolution and crossover/mutation operations, the algorithm identifies optimal SVM configurations that maximize classification margins while minimizing generalization errors. This optimization approach significantly enhances SVM's recognition precision and predictive capability across various application scenarios, particularly in handling high-dimensional and non-linearly separable data. The core implementation utilizes population-based search mechanisms to balance exploration and exploitation, ensuring robust performance improvements while maintaining computational efficiency.