Genetic Algorithm with Decimal Encoding

This MATLAB-based program implements a real-valued genetic algorithm designed to solve various optimization problems. Through the evolutionary process of genetic algorithms, it can find optimal or near-optimal solutions. The implementation utilizes real-number encoding where chromosomes represent solutions as vectors of floating-point numbers, making it particularly suitable for continuous optimization problems. Key algorithmic components include tournament selection, simulated binary crossover (SBX), and polynomial mutation operators that maintain solution diversity while exploring the search space. The program features customizable parameters such as population size, crossover probability, mutation rate, and maximum generations, allowing users to adapt the algorithm to specific problem domains including engineering, economics, and biology. The modular code structure enables easy modification of fitness functions and genetic operators, while the intuitive interface supports multiple runs for comparative analysis. Users can model problems, adjust parameters, and execute multiple iterations to obtain and compare different solutions through statistical performance metrics.