  Neuro-evolutionary methods have proven effective in addressing a wide range
of tasks. However, the study of the robustness and generalizability of evolved
artificial neural networks (ANNs) has remained limited. This has immense
implications in the fields like robotics where such controllers are used in
control tasks. Unexpected morphological or environmental changes during
operation can risk failure if the ANN controllers are unable to handle these
changes. This paper proposes an algorithm that aims to enhance the robustness
and generalizability of the controllers. This is achieved by introducing
morphological variations during the evolutionary training process. As a
results, it is possible to discover generalist controllers that can handle a
wide range of morphological variations sufficiently without the need of the
information regarding their morphologies or adaptation of their parameters. We
perform an extensive experimental analysis on simulation that demonstrates the
trade-off between specialist and generalist controllers. The results show that
generalists are able to control a range of morphological variations with a cost
of underperforming on a specific morphology relative to a specialist. This
research contributes to the field by addressing the limited understanding of
robustness and generalizability and proposes a method by which to improve these
properties.
