  The joint optimisation of body-plan and control via evolutionary processes
can be challenging in rich morphological spaces in which offspring can have
body-plans that are very different from either of their parents. This causes a
potential mismatch between the structure of an inherited controller and the new
body. To address this, we propose a framework that combines an evolutionary
algorithm to generate body-plans and a learning algorithm to optimise the
parameters of a neural controller. The topology of this controller is created
once the body-plan of each offspring body-plan is generated. The key novelty of
the approach is to add an external archive for storing learned controllers that
map to explicit `types' of robots (where this is defined with respect the
features of the body-plan). By learning from a controller with an appropriate
structure inherited from the archive, rather than from a randomly initialised
one, we show that both the speed and magnitude of learning increases over time
when compared to an approach that starts from scratch, using two tasks and
three environments. The framework also provides new insights into the complex
interactions between evolution and learning.
