In many different social contexts, communication allows a collective intelligence to emerge. However, a correct way of exchanging information usually requires determined topological configurations of the agents involved in the process. Such a configuration should take into account several parameters, e.g. agents positioning, their proximity and time efficiency of communication. Our aim is to present an algorithm, based on evolutionary programming, which optimizes agents placement on arbitrarily shaped areas. In order to show its ability to deal with arbitrary bi-dimensional topologies, this algorithm has been tested on a set of differently shaped areas that present concavities, convexities and obstacles. This approach can be extended to deal with concrete cases, such as object localization in a delimited area.
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