  Wagner's modularity inducing problem domain is a key contribution to the
study of the evolution of modularity, including both evolutionary theory and
evolutionary computation. We study its behavior under classical genetic
algorithms. Unlike what we seem to observe in nature, the emergence of
modularity is highly conditional and dependent, for example, on the eagerness
of search. In nature, modular solutions generally dominate populations, whereas
in this domain, modularity, when it emerges, is a relatively rare variant.
Emergence of modularity depends heavily on random fluctuations in the fitness
function, with a randomly varied but unchanging fitness function, modularity
evolved far more rarely. Interestingly, high-fitness non-modular solutions
could frequently be converted into even-higher-fitness modular solutions by
manually removing all inter-module edges. Despite careful exploration, we do
not yet have a full explanation of why the genetic algorithm was unable to find
these better solutions.
