Genetic Algorithm Implementation in MATLAB

This MATLAB-implemented genetic algorithm is an optimization method based on evolutionary principles. The algorithm simulates biological evolution processes including selection, crossover, and mutation operations to iteratively improve solutions. Using binary encoding, the algorithm represents potential solutions as binary strings where each bit position (0 or 1) corresponds to specific decision variable values. The implementation features key genetic operations: fitness evaluation calculates solution quality using objective functions, selection employs roulette wheel or tournament methods to choose parents, crossover (typically single-point) combines parent chromosomes, and mutation introduces diversity by flipping random bits. Through iterative generations, the algorithm effectively explores the solution space to converge toward optimal or near-optimal solutions. The package includes detailed documentation covering algorithm parameters (population size, mutation rate, crossover probability), MATLAB function descriptions (ga_main for algorithm control, fitness_func for objective evaluation), and implementation examples for various optimization problems. This comprehensive documentation facilitates user understanding and practical application across different optimization scenarios.