Is situated evolution an alternative for classical evolution?

In this paper we present an evolutionary method that can deal with the specific problem requirements of adaptivity, scalability and robustness. These requirements are increasingly observed in the areas of pervasive and autonomic computing, and the area of collective robotics. For the purpose of this paper, we concentrate on the problem domain of collective robotics, and more specifically on a surveillance task for such a collective. We present the Situated Evolution Method as a viable alternative for classical evolutionary methods specifically for problem domains with the aforementioned requirements. By means of simulation experiments for a surveillance task, we show that our new method does not lose performance in comparison with a classical evolutionary method, and it has the important design and deployment advantage of being adaptive, scalable and robust.