A Genetic Algorithm is a search heuristic inspired by the process of natural selection. It is used to solve optimization and search problems by mimicking the way biological evolution works. In this method, potential solutions are represented as "chromosomes," which undergo processes like selection, crossover, and mutation to evolve over generations, gradually improving the quality of solutions. The algorithm starts with a population of random solutions and evaluates their performance based on a defined fitness function. The best-performing solutions are selected to create new offspring, which inherit traits from their parents. This iterative process continues until a satisfactory solution is found or a set number of generations is reached.