  A genetic algorithm (GA) is a search-based optimization technique based on
the principles of Genetics and Natural Selection. We present an algorithm which
enhances the classical GA with input from quantum annealers. As in a classical
GA, the algorithm works by breeding a population of possible solutions based on
their fitness. However, the population of individuals is defined by the
continuous couplings on the quantum annealer, which then give rise via quantum
annealing to the set of corresponding phenotypes that represent attempted
solutions. This introduces a form of directed mutation into the algorithm that
can enhance its performance in various ways. Two crucial enhancements come from
the continuous couplings having strengths that are inherited from the fitness
of the parents (so-called nepotism) and from the annealer couplings allowing
the entire population to be influenced by the fittest individuals (so-called
quantum-polyandry). We find our algorithm to be significantly more powerful on
several simple problems than a classical GA.
