Networks of evolution: modelling and deconstructing genetic algorithms using dynamic networks.

Evolutionary computing has benefited several fields such as psychology, economics, and of course, computer science. These algorithms can tackle challenging real-world optimisation problems using selection, crossover, and mutation operators. Despite the rich literature examining evolutionary algorithms, there are unanswered questions concerning their behaviour and the interplay between operators. This work models genetic algorithms as dynamic networks where nodes represent the population and edges describe the inheritance link between individuals. Using the interaction networks as a proxy, we assessed the impact of different parameters, optimisation problems and operators (i.e. selection, crossover, mutation) on the algorithm's behaviour.