  Taking precautions before or during the start of a virus outbreak can heavily
reduce the number of infected. The question which individuals should be
immunized in order to mitigate the impact of the virus on the rest of
population has received quite some attention in the literature. The dynamics of
the of a virus spread through a population is often represented as information
spread over a complex network. The strategies commonly proposed to determine
which nodes are to be selected for immunization often involve only one
centrality measure at a time, while often the topology of the network seems to
suggest that a single metric is insufficient to capture the influence of a node
entirely.
  In this work we present a generic method based on a genetic algorithm (GA)
which does not rely explicitly on any centrality measures during its search but
only exploits this type of information to narrow the search space. The fitness
of an individual is defined as the estimated expected number of infections of a
virus following SIR dynamics. The proposed method is evaluated on two contact
networks: the Goodreau's Faux Mesa high school and the US air transportation
network. The GA method manages to outperform the most common strategies based
on a single metric for the air transportation network and its performance is
comparable with the best performing strategy for the high school network.
