Constrained evolutionary algorithms for epidemic spreading curing policy

The design and developments of policies aiming to control and contain spreading processes when resources are limited is an important problem in many application domains dealing with resource allocation, such as public health and network security. This problem, referred as Optimal Curing Policy (OCP) problem, can be formalized as a constrained minimization problem by relying on the approximated heterogeneous N-Intertwined Mean-Field Approximation (NIMFA) model of the SIS spreading process. In this paper, an approach which combines Differential Evolution and Genetic Algorithms is proposed to solve the OCP problem. The hybridization leverages the best characteristics of the two methods to produce high quality solutions in an efficient and effective way. An extensive experimentation on both real-world and synthetic networks shows that the approach is able to outperform a standard solver for semidefinite programming.