This project aims to minimize the Cross-in-Tray function by utilizing the Genetic and the Simulated Annealing algorithm as optimization methods. The Genetic algorithm is a method of optimization that is inspired by the process of natural selection. It uses a population of candidate solutions and applies genetic operators such as selection, crossover, and mutation to generate new solutions. The Simulated Annealing algorithm is a optimization technique that is inspired by the annealing process in metallurgy. It uses random moves to explore the solution space and gradually reduces the temperature to converge to a global optimum. This project is focused on finding the lowest possible value of the Cross-in-Tray function by utilizing the Genetic algorithm and Simulated Annealing algorithm. The Genetic algorithm is a method that mimics the process of natural selection by using a group of potential solutions and applying genetic operations such as selection, crossover and mutation to generate new solutions. The Simulated Annealing algorithm is a optimization technique that is inspired by the annealing process in metallurgy. It uses random steps to explore the space of solutions and gradually decreasing the temperature to approach the optimal global solution. By applying these two algorithms to the Cross-in-Tray function, the project aims to find the minimum value of this function.
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