  Quality diversity is a recent family of evolutionary search algorithms which
focus on finding several well-performing (quality) yet different (diversity)
solutions with the aim to maintain an appropriate balance between divergence
and convergence during search. While quality diversity has already delivered
promising results in complex problems, the capacity of divergent search
variants for quality diversity remains largely unexplored. Inspired by the
notion of surprise as an effective driver of divergent search and its
orthogonal nature to novelty this paper investigates the impact of the former
to quality diversity performance. For that purpose we introduce three new
quality diversity algorithms which employ surprise as a diversity measure,
either on its own or combined with novelty, and compare their performance
against novelty search with local competition, the state of the art quality
diversity algorithm. The algorithms are tested in a robot navigation task
across 60 highly deceptive mazes. Our findings suggest that allowing surprise
and novelty to operate synergistically for divergence and in combination with
local competition leads to quality diversity algorithms of significantly higher
efficiency, speed and robustness.
