Optimizing SVM Parameters Using Genetic Algorithm (GA)

Optimizing Support Vector Machine (SVM) parameters using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) can significantly improve classification accuracy. These evolutionary algorithms systematically identify optimal parameter combinations that maximize model performance. Genetic Algorithm mimics natural selection processes through operations like selection, crossover, and mutation to evolve toward optimal solutions. In code implementation, GA typically involves initializing a population of parameter sets, evaluating fitness using cross-validation accuracy, and iteratively applying genetic operators. Particle Swarm Optimization simulates social behavior patterns where particles (parameter sets) navigate the solution space by tracking personal and global best positions. The algorithm implementation requires defining velocity update equations and position adjustments based on fitness evaluations. Through systematic exploration of hyperparameter spaces (like SVM's C and gamma parameters), these optimization techniques eliminate manual trial-and-error approaches, enabling automated discovery of parameter configurations that enhance machine learning model performance while maintaining computational efficiency.