J. Urzelai et D. Floreano, Evolution of adaptive synapses: Robots with fast adaptive behavior in new environments, EVOL COMPUT, 9(4), 2001, pp. 495-524
This paper is concerned with adaptation capabilities of evolved neural cont
rollers. We propose to evolve mechanisms for parameter self-organization in
stead of evolving the parameters themselves. The method consists of encodin
g a set of local adaptation rules that synapses follow while the robot free
ly moves in the environment. In the experiments presented here, the perform
ance of the robot is measured in environments that are different in signifi
cant ways from those used during evolution. The results show that evolution
ary adaptive controllers solve the task much faster and better than evoluti
onary standard fixed-weight controllers, that the method scales up well to
large architectures, and that evolutionary adaptive controllers can adapt t
o environmental changes that involve new sensory characteristics (including
transfer from simulation to reality and across different robotic platforms
) and new spatial relationships.