The goal of this research is to explore coevolutionary learning, a variant of genetic algorithms in which candidate solutions co-evolve with the problems on which they are tested. The idea is that the candidate solutions, called "hosts", improve over time via evolution, while at the same time the problems on which they are tested, called "parasites", evolve to become increasingly difficult by learning to exploit specific weaknesses in the hosts.