  Designing controllers for robot swarms is challenging, because human
developers have typically no good understanding of the link between the details
of a controller that governs individual robots and the swarm behavior that is
an indirect result of the interactions between swarm members and the
environment. In this paper we investigate whether an evolutionary approach can
mitigate this problem. We consider a very challenging task where robots with
limited sensing and communication abilities must follow the gradient of an
environmental feature and use Differential Evolution to evolve a neural network
controller for simulated robots. We conduct a systematic study to measure the
flexibility and scalability of the method by varying the size of the arena and
number of robots in the swarm. The experiments confirm the feasibility of our
approach, the evolved robot controllers induced swarm behavior that solved the
task. We found that solutions evolved under the harshest conditions (where the
environmental clues were the weakest) were the most flexible and that there is
a sweet spot regarding the swarm size. Furthermore, we observed collective
motion of the swarm, showcasing truly emergent behavior that was not
represented in- and selected for during evolution.
