MATLAB Implementation of an Enhanced Genetic Algorithm

This enhanced genetic algorithm program is designed to optimize parameter selection more effectively. The genetic algorithm serves as a robust optimization method that efficiently addresses parameter optimization challenges. By implementing key improvements to the standard genetic algorithm structure, this program achieves higher computational efficiency and solution accuracy, making it suitable for diverse application domains. The implementation includes enhanced selection mechanisms using tournament or roulette wheel methods, adaptive crossover and mutation operators with dynamic probability adjustments, and elitism preservation to maintain best solutions across generations. In both engineering applications and scientific research, optimal parameter selection remains a critical task. Therefore, this improved genetic algorithm implementation provides significant value for solving real-world optimization problems, featuring capabilities like constraint handling, convergence monitoring, and customizable fitness functions that can be adapted to specific problem requirements through MATLAB's flexible programming environment.