  In evolutionary robotics, jointly optimising the design and the controller of
robots is a challenging task due to the huge complexity of the solution space
formed by the possible combinations of body and controller. We focus on the
evolution of robots that can be physically created rather than just simulated,
in a rich morphological space that includes a voxel-based chassis, wheels, legs
and sensors. On the one hand, this space offers a high degree of liberty in the
range of robots that can be produced, while on the other hand introduces a
complexity rarely dealt with in previous works relating to matching controllers
to designs and in evolving closed-loop control. This is usually addressed by
augmenting evolution with a learning algorithm to refine controllers. Although
several frameworks exist, few have studied the role of the \textit{evolutionary
dynamics} of the intertwined `evolution+learning' processes in realising
high-performing robots. We conduct an in-depth study of the factors that
influence these dynamics, specifically: synchronous vs asynchronous evolution;
the mechanism for replacing parents with offspring, and rewarding goal-based
fitness vs novelty via selection. Results show that asynchronicity combined
with goal-based selection and a `replace worst' strategy results in the highest
performance.
