  Control theory has seen recently impactful applications in network science,
especially in connections with applications in network medicine. A key topic of
research is that of finding minimal external interventions that offer control
over the dynamics of a given network, a problem known as network
controllability. We propose in this article a new solution for this problem
based on genetic algorithms. We tailor our solution for applications in
computational drug repurposing, seeking to maximise its use of FDA-approved
drug targets in a given disease-specific protein-protein interaction network.
We show how our algorithm identifies a number of potentially efficient drugs
for breast, ovarian, and pancreatic cancer. We demonstrate our algorithm on
several benchmark networks from cancer medicine, social networks, electronic
circuits, and several random networks with their edges distributed according to
the Erd\H{o}s-R\'{e}nyi, the small-world, and the scale-free properties.
Overall, we show that our new algorithm is more efficient in identifying
relevant drug targets in a disease network, advancing the computational
solutions needed for new therapeutic and drug repurposing approaches.
