  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.
