Optimizing Support Vector Machine Parameters Using Genetic Algorithms

In this document, I present a custom-developed program that employs genetic algorithms to optimize Support Vector Machine (SVM) parameters. The implementation features a modular design with clear separation between the genetic algorithm optimization module and SVM configuration handler. The genetic algorithm component includes key functions for population initialization, fitness evaluation using cross-validation accuracy, tournament selection, simulated binary crossover, and polynomial mutation operators. The program systematically evolves SVM hyperparameters (like kernel parameters and penalty factor C) through generations, demonstrating how bio-inspired optimization techniques can enhance machine learning model performance. I provide detailed explanations of chromosome encoding schemes, fitness function design that prevents overfitting, and termination criteria implementation. Sample code showcases practical integration with libsvm libraries while maintaining computational efficiency through elitism preservation and adaptive mutation rates. This educational implementation includes comprehensive comments and visualization tools to track convergence behavior, making it particularly suitable for beginners exploring optimization techniques in machine learning.