Jim Rutt defines 'evolutionary computing' as a subfield of artificial intelligence and computational science that leverages mechanisms inspired by biological evolution to develop algorithms for solving complex problems. Drawing from principles like natural selection, mutation, recombination, and heredity, evolutionary computing employs these processes to evolve solutions over successive generations. By simulating the adaptive qualities of natural systems, it aims to optimize tasks in various domains including optimization problems, machine learning, and automated engineering design. Rutt emphasizes the parallels to Darwinian evolution, pointing out that the iterative process of selection and variation allows systems to adapt and improve, ultimately leading to robust and efficient solutions that might be challenging to achieve through traditional deterministic methods.

See also: evolution, emergence, artificial intelligence, game theory