Genetic Algorithm for Function Optimization Problems

This project implements a genetic algorithm in MATLAB to solve function optimization problems. The algorithm iteratively searches for optimal solutions while tracking the number of iterations required for convergence. Key implementation aspects include: The core algorithm employs selection, crossover, and mutation operations to evolve candidate solutions. The code structure typically involves: - Population initialization function generating random solutions within specified boundaries - Fitness evaluation module calculating objective function values - Selection mechanism (e.g., roulette wheel or tournament selection) for choosing parents - Crossover operator (single-point or uniform crossover) for creating offspring - Mutation function introducing random variations to maintain diversity For performance enhancement, the implementation includes parameter adjustment capabilities allowing users to modify: - Population size and generation count - Crossover and mutation probabilities - Selection pressure parameters Advanced optimization features can incorporate adaptive strategies such as: - Dynamic mutation rates that adjust based on population diversity - Elitism preservation ensuring best solutions carry over to next generations - Convergence detection mechanisms to stop iterations when improvement plateaus The solution includes a results analysis section that: - Visualizes convergence curves showing fitness improvement over generations - Statistical evaluation of solution quality and algorithm reliability - Comparative analysis against known optimal values or alternative methods - Sensitivity analysis of key parameters on solution accuracy and convergence speed The code provides comprehensive output including final optimal solution values, corresponding fitness scores, total iteration count, and convergence metrics for performance assessment.