Adaptive Genetic Algorithm Example

This is an example of an adaptive genetic algorithm implemented in MATLAB, designed to demonstrate improvements through comparison with conventional methods. The adaptive genetic algorithm is an optimization technique based on genetic algorithms that automatically adjusts algorithm parameters and operations according to problem characteristics and requirements to enhance optimization effectiveness. This implementation includes key features such as dynamic crossover and mutation rates that adapt based on population fitness diversity, elite preservation mechanisms, and fitness scaling techniques. The code demonstrates practical implementation of selection operators (tournament or roulette wheel), crossover methods (single-point or uniform), and mutation operations with adaptive probabilities. This example helps users better understand and apply adaptive genetic algorithms, enabling them to achieve superior results when solving real-world optimization problems. The MATLAB code includes functions for population initialization, fitness evaluation, adaptive operator adjustment, and convergence monitoring, providing a comprehensive framework for optimization tasks.