In this paper we aim to examine emergent properties of agent-based models by using evolutionary algorithms. Taking the model of flocking boids as an example, we study and try to understand how the pressure of natural selection towards strategic behavior can result in emergent behavior. Furthermore we investigate how an increase of complexity in the model affects those properties and discover some counter-intuitive behavior in the process.
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