Evolutionary Game Theory has been originally developed and formalized by [247], in order to model the evolution of animal species and it has soon become an important mathematical tool to predict and even design evolution in many fields, others than biology. It mainly focuses on the dynamical evolution of the strategies adopted in a population of interacting individuals, where the notion of equilibrium adopted is that of Evolutionarily Stable Strategy (ESS, [247]), implying robustness against a mutation (i.e. a change in the strategy) of a small fraction of the population. This is a stronger condition than the standard Nash equilibrium concept, which requires robustness against deviation of a single user. On the importance of the ESS for understanding the evolution of species, Dawkins writes in his book "The Selfish Gene" [290]: "we may come to look back on the invention of the ESS concept as one of the most important advances in evolutionary theory since Darwin." He further specifies: "Maynard Smith's concept of the ESS will enable us, for the first time, to see clearly how a collection of independent selfish entities can come to resemble a single organized whole. Evolutionary game theory is nowadays considered as an important enrichment of game theory and it's applied in a wide variety of fields, spanning from social sciences [107] to computer science. Some examples of applications in computer science can be found in multiple access protocols [250], multihoming[240] and resources competition in the Internet [298]. This theory is usually adopted in situations where individuals belonging to a very large population are matched in random pairwise interactions. In classical evolutionary games (EG), each individual constitutes a selfish player involved in a non-cooperative game, maximizing its own utility, also said fitness, since in 7