Effect of Degree Distribution on Evolutionary Search

TL;DR

This paper investigates how the degree distribution and modularity of networks influence the effectiveness of evolutionary algorithms, revealing that certain degree distributions facilitate easier problem solving.

Contribution

It introduces a method to generate modular networks with specific degree distributions and demonstrates their impact on evolutionary algorithm performance.

Findings

Modularity enhances genetic algorithm performance over hill climbers.

Heavy-tailed degree distributions make problems easier to solve.

Normal degree distributions are less conducive to problem solving.

Abstract

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.