In this paper an evolutionary method consisting of encoding a set of local adaptation rules that synapses obey while a robot freely moves in the environment is compared to standard evolution of fixed-weight control networks. The results show ha evolutionary adaptive controllers can adapt online without additional evolutionary training to strong environmental changes where instead the performance of evolutionary fixed- weight controllers is significantly degraded. Two cases are described: transfer of evolved controllers from simulated to real robots and across different robotic platforms that vary in size, shape, and sensor response profile. In both cases evolved adaptive controllers autonomously and quickly adjust synaptic weights to successfully accomplish the task in the new conditions.