The genetic assimilation of learned behaviour was introduced to the
wider evolutionary computation field by the classic simulation of
Hinton and Nowlan.  Subsequent studies have analysed and extended their
initial framework, contributing to the understanding of the often
counterintuitive relationship between evolution and learning.  We add to
this increasing body of literature by presenting an evolving population
of neural networks that plainly exhibit the Baldwin effect.  Phenotypic
plasticity, embodied in the literal learning rate of the neural
networks, is evolved along with the network connection weights.
Significantly, high levels of plasticity do not cause the population to
genetically stagnate once correct behaviour can be learned.  Rather,
continuing inter-population competition drives the levels of learning
down as beneficial behaviour becomes genetically specified.
By observing the evolving learning rate of the agent population, and by
comparing learned and innate agent responses, we demonstrate the Baldwin
effect in its entirety.