  This paper demonstrates that a set of behaviours evolved in simulation on a
miniature robot (epuck) can be transferred to a much larger scale platform (a
virtual Pioneer P3-DX) that also differs in shape, sensor type, sensor
configuration and programming interface. The chosen architecture uses a
reinforcement learning-assisted genetic algorithm to evolve the epuck
behaviours, which are encoded as a genetic sequence. This sequence is then used
by the Pioneers as part of an adaptive, idiotypic artificial immune system
(AIS) control architecture. Testing in three different simulated worlds shows
that the Pioneer can use these behaviours to navigate and solve object-tracking
tasks successfully, as long as its adaptive AIS mechanism is in place.
