The rise and fall of learning: a neural network model of the genetic assimilation of acquired traits

The genetic assimilation of learned behaviour was introduced to a wider evolutionary computation field by the classic simulation of Hinton and Nowlan (1987). 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 the learned and innate agent responses, we demonstrate the Baldwin effect in its entirety.