  The survivable network concept refers to contexts where the wireless
communication between ground agents needs to be maintained as much as possible
at all times, regardless of any adverse conditions that may arise. In this
paper we propose a nature-inspired approach to survivable networks, in which we
bring together swarm intelligence and evolutionary computation. We use an
on-line real-time Genetic Algorithm to optimize the movements of an UAV swarm
towards maintaining communication between the ground agents. The proposed
approach models the ground agents and the UAVs as boids-based swarms, and
optimizes the movement of the UAVs using different instances of the GA running
independently on each UAV. The UAV coordination mechanism is an implicit one,
embedded in the fitness function of the Genetic Algorithm instances. The
behaviors of the individual UAVs emerge into an aggregated optimization of the
overall network survivability. The results show that the proposed approach is
able to maintain satisfactory network survivability levels regardless of the
ground agents' movements, including for cases as complex as random walks.
