Discuss the role of genetic algorithms in optimization problems and how they mimic natural evolutionary processes to find solutions.

Genetic algorithms (GAs) solve optimization problems by mimicking natural evolutionary processes such as selection, crossover, and mutation. They start with a population of candidate solutions, iteratively applying these processes to evolve towards optimal solutions. GAs are particularly effective for complex, multi-modal landscapes where traditional methods struggle, offering robustness, adaptability, and the ability to find global optima by exploring a wide search space.