This project aims to minimize the Cross-in-Tray function using the Genetic Algorithm and Simulated Annealing as optimization methods. The Genetic Algorithm, inspired by natural selection, employs a population of candidate solutions and applies genetic operators such as selection, crossover, and mutation to generate new solutions. On the other hand, the Simulated Annealing algorithm, inspired by the annealing process in metallurgy, explores the solution space through random moves and gradually reduces the temperature to converge to a global optimum. By leveraging these two algorithms, this project seeks to find the lowest possible value of the Cross-in-Tray function, effectively identifying the function's global minimum.
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