Evolutionary algorithms are a type of optimization algorithm inspired by the process of natural selection and evolutionary theory. These algorithms work by creating a population of potential solutions to a problem and then iteratively evolving the population to find better solutions. This involves selecting individuals from the population based on their fitness, recombining them to create new individuals, and introducing random mutations. Evolutionary algorithms are used in a wide range of applications, including engineering design, scheduling problems, and machine learning. They are particularly useful for problems that are non-linear, complex, or have a large search space, as they are able to explore a wide range of possible solutions. Evolutionary algorithms are also known for their ability to find solutions that may be difficult to reach using traditional optimization methods.