MATLAB Implementation of Adaptive Genetic Algorithm

This adaptive genetic algorithm implemented in MATLAB automatically adjusts crossover and mutation probabilities according to fitness values, providing optimal parameter settings relative to specific optimal solutions. The algorithm's key advantage over traditional genetic algorithms lies in its elimination of manual parameter specification, reducing human intervention while significantly enhancing search efficiency. Implementation typically involves calculating probability adjustments using fitness-based formulas, where higher fitness solutions receive lower mutation rates to preserve good traits, while lower fitness solutions get higher rates to promote diversity. The core MATLAB functions would include: - Fitness evaluation and normalization routines - Dynamic probability calculation modules - Selection mechanisms (roulette wheel/tournament) - Adaptive crossover and mutation operators The algorithm demonstrates superior performance in solving complex optimization problems, exhibiting enhanced global search capability and convergence properties. Its practical applications span various fields including engineering optimization, machine learning parameter tuning, and financial modeling, making it a valuable tool for complex problem-solving scenarios.