Analysis of Evolutionary Diversity Optimisation for the Maximum Matching Problem

Jonathan Gadea Harder, Aneta Neumann, Frank Neumann

Abstract: This paper delves into the enhancement of solution diversity in evolutionary algorithms (EAs) for the maximum matching problem, with a particular focus on complete bipartite graphs and paths. We utilize binary string encoding for matchings and employ Hamming distance as the metric for measuring diversity, aiming to maximize it. Central to our research is the \((\mu +1)\)-EA\(_D\) and 2P-EA\(_D\), applied for diversity optimization, which we rigorously analyze both theoretically and empirically.