Genetic Algorithms: The Design of Innovation illustrates how to design and implement scalable genetic algorithms that solve hard problems quickly, reliably, and accurately. This revised edition of the landmark The Design of Innovation includes recent results and new groundbreaking material. The core chapters have been updated and some chapters have been thoroughly rewritten. The chapter on scalable GA design introduces other key techniques, including the Dependency Structure Matrix GA (DSMGA), which sheds light on probabilistic model builders such as the Bayesian Optimization Algorithm. A major new chapter demonstrates practical scalability of GAs on a problem with over a billion variables, and shows how these results can be used to obtain routine solutions to important problems. Genetic Algorithms is an essential reference for the innovation researcher from the social and behavioral sciences, the natural sciences, the humanities, or the arts or for the specialist in GAs and evolutionary computation.