  Random Boolean networks (RBNs) are frequently employed for modelling complex
systems driven by information processing, e.g. for gene regulatory networks
(GRNs). Here we propose a hierarchical adaptive RBN (HARBN) as a system
consisting of distinct adaptive RBNs - subnetworks - connected by a set of
permanent interlinks. Information measures and internal subnetworks topology of
HARBN coevolve and reach steady-states that are specific for a given network
structure. We investigate mean node information, mean edge information as well
as a mean node degree as functions of model parameters and demonstrate HARBN's
ability to describe complex hierarchical systems.
