  This paper introduces a method to generate hierarchically modular networks
with prescribed node degree list and proposes a metric to measure network
modularity based on the notion of edge distance. The generated networks are
used as test problems to explore the effect of modularity and degree
distribution on evolutionary algorithm performance. Results from the
experiments (i) confirm a previous finding that modularity increases the
performance advantage of genetic algorithms over hill climbers, and (ii)
support a new conjecture that test problems with modularized constraint
networks having heavy-tailed right-skewed degree distributions are more easily
solved than test problems with modularized constraint networks having
bell-shaped normal degree distributions.
