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Game theory is a broadly accepted tool for resource optimization in cognitive radio networks. By modeling a resource management problem as a game, some aspects of a real-world implementation have to be relaxed. For instance, the number of players in a game is conventionally assumed constant but in real networks it changes over time. Furthermore, the available computing power of wireless sensor nodes is limited because of energy and simple hardware constraints. Hence, games require utility functions with low complexity. In this paper, some aspects of evolutionary game theory are borrowed to address these issues. Our approach results in a simple algorithm, which avoids theoretical modeling aspects not hold in real world applications.