  Evolving Counter-Propagation Neuro-Controllers (CPNCs), rather than the
traditional Feed-Forward Neuro-Controllers (FFNCs), has recently been suggested
and tested using simulated robot navigation. It has been demon-strated that
both convergence rate and final performance obtained by evolving CPNCs are
superior to those obtained by evolving FFNCs. In this paper the maze
generalization features of both types of evolved navigation controllers are
examined. For this purpose the controllers are tested in an environment that
drastically differs from the one used for their training. Moreover, a
comparison is carried out of results obtained by single-objective and
multi-objective evolution approaches. Using a simulated case-study, the maze
generalization capability of the evolved CPNCs is highlighted in both the
single and multi-objective cases. In contrast, the evolved FFNCs are found to
lack such capabilities in both approaches.
