  A core challenge of evolutionary search is the need to balance between
exploration of the search space and exploitation of highly fit regions.
Quality-diversity search has explicitly walked this tightrope between a
population's diversity and its quality. This paper extends a popular
quality-diversity search algorithm, MAP-Elites, by treating the selection of
parents as a multi-armed bandit problem. Using variations of the
upper-confidence bound to select parents from under-explored but potentially
rewarding areas of the search space can accelerate the discovery of new regions
as well as improve its archive's total quality. The paper tests an indirect
measure of quality for parent selection: the survival rate of a parent's
offspring. Results show that maintaining a balance between exploration and
exploitation leads to the most diverse and high-quality set of solutions in
three different testbeds.
