  Swarms of molecular robots are a promising approach to create specific shapes
at the microscopic scale through self-assembly. However, controlling their
behavior is a challenging problem as it involves complex non-linear dynamics
and high experimental variability. Hand-crafting a molecular controller will
often be time-consuming and give sub-optimal results. Optimization methods,
like the bioNEAT algorithm, were previously employed to partially overcome
these difficulties, but they still had to cope with deceptive high-dimensional
search spaces and computationally expensive simulations. Here, we describe a
novel approach to solve this problem by using MAP-Elites, an algorithm that
searches for both high-performing and diverse solutions. We then apply it to a
molecular robotic framework we recently introduced that allows sensing,
signaling and self-assembly at the micro-scale and show that MAP-Elites
outperforms previous approaches. Additionally, we propose a surrogate model of
micro-robots physics and chemical reaction dynamics to reduce the computational
costs of simulation. We show that the resulting methodology is capable of
optimizing controllers with similar accuracy as when using only a full-fledged
realistic model, with half the computational budget.
