Evolutionary algorithms are optimization techniques inspired by natural selection and genetics. These algorithms evolve solutions to problems by iteratively selecting, mutating, and recombining candidates to find the best outcomes. Common applications include optimizing complex systems, solving scheduling problems, and improving neural network architectures. Genetic algorithms are a well-known type of evolutionary algorithm.