Function Extremum Optimization Using Real-Coded Genetic Algorithm

This MATLAB-based program implements function extremum optimization using real-coded genetic algorithms. The solution employs real-number encoding to represent variables directly, eliminating the need for binary conversion and improving precision for continuous optimization problems. The algorithm utilizes key genetic operations including tournament selection, simulated binary crossover (SBX), and polynomial mutation to maintain population diversity while converging toward optimal solutions. The MATLAB implementation leverages built-in functions for efficient matrix operations and provides customizable parameters for population size, crossover probability, mutation rate, and termination criteria. The program structure includes separate modules for initialization, fitness evaluation, selection, crossover, and mutation operations, allowing easy modification for different objective functions. Users can input custom objective functions through function handles and configure algorithm parameters to solve various extremum optimization problems. The implementation includes convergence plotting capabilities to visualize optimization progress and performance metrics to evaluate solution quality. This approach enhances function performance by efficiently exploring high-dimensional search spaces while maintaining computational efficiency through vectorized MATLAB operations.