  The study of optimal control of quantum annealing by modulating the pace of
evolution and by introducing a counterdiabatic potential has gained significant
attention in recent times. In this work, we present a numerical approach based
on genetic algorithms to improve the performance of quantum annealing, which
evades the Landau-Zener transitions to navigate to the ground state of the
final Hamiltonian with high probability. We optimize the annealing schedules
starting from polynomial ansatz by treating their coefficients as chromosomes
of the genetic algorithm. We also explore shortcuts to adiabaticity by
computing a practically feasible $k$-local optimal driving operator, showing
that even for $k=1$ we achieve substantial improvement of the fidelity over the
standard annealing solution. With these genetically optimized annealing
schedules and/or optimal driving operators, we are able to perform quantum
annealing in relatively short time-scales and with larger fidelity compared to
traditional approaches.
