Function Extremum Optimization Using Real-Coded Genetic Algorithm

This program utilizes a real-coded genetic algorithm to perform comprehensive and accurate function extremum optimization. The implementation incorporates advanced genetic algorithm techniques including tournament selection, simulated binary crossover (SBX), and polynomial mutation operators to ensure better optimization results. The algorithm maintains population diversity through real-number encoding, where each chromosome represents a candidate solution as a vector of real values. Key functions include fitness evaluation, selection, crossover, and mutation operations, with elitism preservation to maintain the best solutions across generations. We prioritize program stability and reliability by implementing robust termination criteria (such as convergence thresholds and maximum generation limits) and boundary constraint handling mechanisms. The code structure ensures proper exception handling and validation checks for various input scenarios. By using this program, you can effectively optimize your functions with improved precision in results, supported by detailed convergence analysis and parameter tuning capabilities.