MATLAB Implementation of Genetic Algorithm for Constrained Optimization Problems

This MATLAB implementation provides a genetic algorithm program designed to solve optimization problems with constraints. Genetic algorithms are heuristic optimization techniques that simulate natural evolutionary processes to find optimal solutions. They are widely applicable in various fields including engineering design, machine learning, and data mining. The implementation includes key components such as: - Population initialization with feasible solutions - Fitness function evaluation considering constraint penalties - Selection operations using tournament or roulette wheel methods - Crossover and mutation operators for solution space exploration - Constraint handling through penalty functions or specialized operators The algorithm iteratively improves solutions through generations, balancing exploration of new solutions with exploitation of promising ones. By implementing this genetic algorithm in MATLAB, we can effectively tackle complex optimization challenges while satisfying both constraint conditions and optimization objectives. This approach enables researchers and engineers to obtain superior results for constrained optimization problems, with the flexibility to customize genetic operators and constraint handling strategies according to specific problem requirements.