  At the end of 2020, policy responses to the SARS-CoV-2 outbreak have been
shaken by the emergence of virus variants, impacting public health and policy
measures worldwide. The emergence of these strains suspected to be more
contagious, more severe, or even resistant to antibodies and vaccines, seem to
have taken by surprise health services and policymakers, struggling to adapt to
the new variants constraints. Anticipating the emergence of these mutations to
plan ahead adequate policies, and understanding how human behaviors may affect
the evolution of viruses by coevolution, are key challenges. In this article,
we propose coevolution with genetic algorithms (GAs) as a credible approach to
model this relationship, highlighting its implications, potential and
challenges. Because of their qualities of exploration of large spaces of
possible solutions, capacity to generate novelty, and natural genetic focus,
GAs are relevant for this issue. We present a dual GA model in which both
viruses aiming for survival and policy measures aiming at minimising infection
rates in the population, competitively evolve. This artificial coevolution
system may offer us a laboratory to "debug" our current policy measures,
identify the weaknesses of our current strategies, and anticipate the evolution
of the virus to plan ahead relevant policies. It also constitutes a decisive
opportunity to develop new genetic algorithms capable of simulating much more
complex objects. We highlight some structural innovations for GAs for that
virus evolution context that may carry promising developments in evolutionary
computation, artificial life and AI.
