Since Hinton and Nowlan introduced the Baldwin effect
to the evolutionary computation community, agent-based
studies of genetic assimilation have uncovered many details of the
dynamic processes involved. In a previous paper, we demonstrated
genetic assimilation with a simple food/toxin discrimination task
using neural network agents that could evolve their learning rate.
The study reported in this paper investigated the genetic
assimilation of more complex learning tasks.
Kauffman's NK landscape
model, which can generate
landscapes with a variable degree of correlation, was used to define
learning tasks of varying levels of complexity. Simulations
indicate an increased tendency of genetic assimilation to occur as
the complexity of the learning task decreases and the environmental
stability increases. These results are explained in terms of the
shifting balance between the evolutionary costs and benefits of
learning.