Evolutionary algorithms (EAs) have grown into a mature field of research in optimization, and have proven to be effective and robust problem-solvers for a broad range of static real-world optimization problems. Yet, since they are based on the principles of natural evolution, and since natural evolution is a dynamic process in a changing environment, EAs are also well suited to dynamic optimization problems. This volume provides a survey on research in the area and shows how EAs can be used to: continuously and efficiently adapt a solution to a changing environment; find a trade-off between solution quality and adaptation cost; find robust solutions whose quality is insensitive to changes in the environment; and find flexible solutions that can be easily adapted when necessary. This resource should prove useful to researchers and professionals who are dealing with dynamic and stochastic optimization problems, and who are interested in applying local search heuristics, such as evolutionary algorithms.