Decimal Genetic Algorithm Program with Real-Valued Encoding

This article presents a MATLAB-implemented genetic algorithm program utilizing real-valued encoding to solve decimal optimization problems. Genetic algorithms are optimization techniques that simulate natural selection and adaptation processes from biological evolution. The real-valued encoding genetic algorithm represents solutions as continuous real numbers, making it particularly effective for optimization problems involving continuous variables. The implementation includes key components such as initialization of real-coded populations, fitness evaluation, tournament selection, simulated binary crossover (SBX), and polynomial mutation operators. The algorithm maintains diversity through specialized genetic operators designed for real-valued representations, avoiding the limitations of binary encoding when handling decimal problem spaces. We demonstrate this algorithm's superiority in solving decimal problems by showing how it efficiently explores continuous search spaces without requiring discretization, thus preserving solution precision and convergence properties.