Simple Genetic Algorithm Optimization for Support Vector Machine Parameters

This text introduces a simple MATLAB program that utilizes genetic algorithms to optimize Support Vector Machine (SVM) parameters. The implementation demonstrates a practical approach where the genetic algorithm handles parameter tuning through evolutionary operations like selection, crossover, and mutation, while the SVM classification accuracy serves as the fitness function. Specifically, the code includes functions for population initialization, fitness calculation using SVM cross-validation, and genetic operators that modify hyperparameters like kernel parameters and penalty factors. This program serves as an excellent learning resource for beginners, providing hands-on experience with both evolutionary computation and machine learning optimization techniques. Through detailed code comments and modular structure, learners can understand how to integrate bio-inspired algorithms with statistical learning methods. The implementation also showcases practical considerations such as encoding schemes for SVM parameters and termination criteria for the optimization process. By studying this program, beginners can gain fundamental insights into hybrid intelligent systems and apply these concepts to real-world optimization problems in their own projects.