Title: Genetic Algorithm for Epidemic Mitigation by Removing Relationships,
Abstract: Min-SEIS-Cluster is an optimization problem which aims at minimizing the
infection spreading in networks. In this problem, nodes can be susceptible to
an infection, exposed to an infection, or infectious. One of the main features
of this problem is the fact that nodes have different dynamics when interacting
with other nodes from the same community. Thus, the problem is characterized by
distinct probabilities of infecting nodes from both the same and from different
communities. This paper presents a new genetic algorithm that solves the
Min-SEIS-Cluster problem. This genetic algorithm surpassed the current
heuristic of this problem significantly, reducing the number of infected nodes
during the simulation of the epidemics. The results therefore suggest that our
new genetic algorithm is the state-of-the-art heuristic to solve this problem.