An analysis of the effects of clustering in graph-based evolutionary algorithms

Abstract

Recently, there has been increased interest in combining work from the complex networks domain with evolutionary computation to solve challenging search and optimization problems. Typically, individuals in the evolving population occupy a node in a graph (or network) and are only allowed to mate with individuals within their local neighbourhood. The use of specific graph topologies have been shown to alter the population dynamics, which in turn impacts on the ability of the algorithm to find (near)-optimal solutions for a given problem. In this paper, we continue this line of research. Here, we have analyzed the impact of clustering on the performance of graph-based evolutionary models. We h..