Adaptive Genetic Algorithm Implementation in MATLAB

This MATLAB code implements an adaptive genetic algorithm that has undergone extensive debugging and testing, successfully passing multiple experimental validations. The adaptive genetic algorithm is an optimization technique based on genetic algorithms, designed to solve various complex optimization problems. The implementation utilizes MATLAB programming language to create a robust adaptive genetic algorithm framework. The code features carefully designed components including dynamic parameter adjustment mechanisms, fitness-based crossover and mutation rate adaptation, and elitism preservation strategies. Key functions implement population initialization, fitness evaluation, selection operations using roulette wheel or tournament selection, adaptive genetic operators, and convergence monitoring. The code architecture has been optimized to enhance algorithmic efficiency and solution accuracy through vectorized operations and parallel computing capabilities where applicable. This implementation serves as a valuable resource for learning and researching adaptive genetic algorithms, providing clear code structure with comprehensive comments for easy modification and extension.