The iterated prisoner’s dilemma is a famous model of cooperation and conflict in game theory. Its origin can be traced back to the Cold War, and countless strategies for playing it have been proposed so far, either designed by hand or automatically generated by computers. In the 2000s, scholars started focusing on adaptive players, that is, able to classify their opponent’s behavior and adopt an effective counter-strategy. The player presented in this paper, pushes such idea even further: it builds a model of the current adversary from scratch, without relying on any pre-defined archetypes, and tweaks it as the game develops using an evolutionary algorithm; at the same time, it exploits the model to lead the game into the most favorable continuation. Models are compact non-deterministic finite state machines; they are extremely efficient in predicting opponents’ replies, without being completely correct by necessity. Experimental results show that such player is able to win several one-toone games against strong opponents taken from the literature, and that it consistently prevails in round-robin tournaments of different sizes.