  Exposing an Evolutionary Algorithm that is used to evolve robot controllers
to variable conditions is necessary to obtain solutions which are robust and
can cross the reality gap. However, we do not yet have methods for analyzing
and understanding the impact of the varying morphological conditions which
impact the evolutionary process, and therefore for choosing suitable variation
ranges. By morphological conditions, we refer to the starting state of the
robot, and to variations in its sensor readings during operation due to noise.
In this article, we introduce a method that permits us to measure the impact of
these morphological variations and we analyze the relation between the
amplitude of variations, the modality with which they are introduced, and the
performance and robustness of evolving agents. Our results demonstrate that (i)
the evolutionary algorithm can tolerate morphological variations which have a
very high impact, (ii) variations affecting the actions of the agent are
tolerated much better than variations affecting the initial state of the agent
or of the environment, and (iii) improving the accuracy of the fitness measure
through multiple evaluations is not always useful. Moreover, our results show
that morphological variations permit generating solutions which perform better
both in varying and non-varying conditions.
