Genetic Algorithms (GA) and Genetic Programming (GP) are methods used to search for and optimize solutions in large solution spaces. GA/GP use concepts borrowed from natural evolution, such as mutation, cross-over, selection, population, and fitness to generate solutions to problems. If done well, these solutions will become better as the GA/GP runs.

GA/GP has been used in problem domains as diverse as scheduling, database index optimization, circuit board layout, mirror and lens design, game strategies, and robotic walking and swimming. They can also be a lot of fun, and have been used to evolve aesthetically pleasing artwork, melodies, and approximating pictures or paintings using polygons.

GA/GP is fun to play with because often-times an unexpected solution will be created that will give new insight or knowledge. It might also present a novel solution to a problem, one that a human may never generate. Solutions may also be inscrutable, and determining why a solution works is interesting in itself.