1 Introduction
An important objective in evolutionary computation (EC) is to exactly model classes of evolutionary algorithms (EAs) and, further, to be able to draw inferences from these models that enhance theoretical understanding and, hopefully, aid “practitioners” in finding more competent EAs. Early models for GAs, proposed by Holland, Goldberg, Whitley and others in the seventies and eighties were either approximate or not easily scalable [4], [3], [28], [29]. Exact probabilistic models have been developed, such as the dynamical systems model of Vose and collaborators [27], [20]. More recently, an alternative exact approach, based on a coarse graining of the dynamics and directly involving schemata, has been introduced, leading to a spate of both new theoretical results [26], [24], [25], [11], [13], [14] and practical recipes for implementation [7], [12].