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.
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