Genetic Algorithm

In our research, the main genetic algorithm program serves as a crucial component. This search algorithm, based on natural selection and genetic inheritance, is designed to identify optimal solutions. The algorithm primarily consists of three operational phases: selection, crossover, and mutation. During the selection phase, we utilize a fitness function to identify and propagate superior individuals through ranking or tournament selection methods. The crossover phase implements chromosome exchange between two parent individuals using techniques like single-point or multi-point crossover to generate new offspring. In the mutation phase, we introduce random alterations to specific genes through bit-flip or swap mutations to enhance population diversity. Consequently, the genetic algorithm main program stands as an effective methodology for solving optimization problems, with implementations typically involving population initialization, fitness evaluation, and generational evolution loops.