A Genetic Algorithm (GA) is an optimization technique inspired by natural selection and evolution. It operates by encoding potential solutions as "chromosomes" and applying genetic operators such as selection, crossover, and mutation to evolve better solutions over generations. The fittest individuals, based on a predefined objective function, are more likely to pass their traits to the next generation, ensuring gradual improvement. GAs are widely used in complex problem-solving, including machine learning, engineering design, and artificial intelligence, where traditional optimization methods may be inefficient or impractical.