Population network structure impacts genetic algorithm optimisation performance

TL;DR

This paper investigates how different social network structures within genetic algorithms affect their optimization performance, revealing that certain network configurations can significantly enhance results.

Contribution

It introduces the Networked Genetic Algorithm (NGA) framework to study the impact of various population network structures on GA performance.

Findings

Intermediate density networks perform best.

Low average shortest path length improves optimization.

Network structure tuning can enhance GA efficiency.

Abstract

A genetic algorithm (GA) is a search method that optimises a population of solutions by simulating natural evolution. Good solutions reproduce together to create better candidates. The standard GA assumes that any two solutions can mate. However, in nature and social contexts, social networks can condition the likelihood that two individuals mate. This impact of population network structure over GAs performance is unknown. Here we introduce the Networked Genetic Algorithm (NGA) to evaluate how various random and scale-free population networks influence the optimisation performance of GAs on benchmark functions. We show evidence of significant variations in performance of the NGA as the network varies. In addition, we find that the best-performing population networks, characterised by intermediate density and low average shortest path length, significantly outperform the standard complete network GA. These results may constitute a starting point for network tuning and network control: seeing the network structure of the population as a parameter that can be tuned to improve the performance of evolutionary algorithms, and offer more realistic modelling of social learning.