MATLAB Implementation of Genetic Algorithm

Genetic Algorithm is an excellent optimization method capable of effectively solving complex problems. This repository contains a comprehensive MATLAB implementation of Genetic Algorithm that features modular design with key functions for population initialization, fitness evaluation, and evolutionary operations. The code includes configurable parameters for population size, crossover rate, and mutation probability, allowing users to adapt the algorithm to specific problem domains. After downloading and extracting the package, you can directly integrate this optimized implementation into your projects. The architecture follows best practices with separate functions for selection (roulette wheel/tournament), crossover (single-point/two-point), and mutation operators, ensuring maintainability and extensibility. This implementation serves as an ideal starting point for both learning genetic algorithms and solving real-world optimization problems, saving development time while providing a robust foundation for your computational intelligence applications. We hope this resource accelerates your research and practical implementations.