It has already been pointed out that cellular automata and Boolean networks are particularly well-suited for the simulation of simple self-organizing processes, i.e., processes in which no change in the interaction rules takes place. Only in this sense are such processes naturally “simple.” However, real systems, especially social and cognitive ones, are often also capable of adapting to environmental conditions and, if necessary, changing their rules. It is certainly no coincidence that these properties of various real systems are becoming increasingly important in robotics and the development of internet agents. The advantages of being able to work with adaptive units here are obvious. Such adaptive capabilities can be particularly well modeled or realized for numerous problems with evolutionary algorithms, as will be shown in this chapter.