  With the development of fast and massively parallel evaluations in many
domains, Quality-Diversity (QD) algorithms, that already proved promising in a
large range of applications, have seen their potential multiplied. However, we
have yet to understand how to best use a large number of evaluations as using
them for random variations alone is not always effective. High-dimensional
search spaces are a typical situation where random variations struggle to
effectively search. Another situation is uncertain settings where solutions can
appear better than they truly are and naively evaluating more solutions might
mislead QD algorithms. In this work, we propose MAP-Elites-Multi-ES (MEMES), a
novel QD algorithm based on Evolution Strategies (ES) designed to exploit fast
parallel evaluations more effectively. MEMES maintains multiple (up to 100)
simultaneous ES processes, each with its own independent objective and reset
mechanism designed for QD optimisation, all on just a single GPU. We show that
MEMES outperforms both gradient-based and mutation-based QD algorithms on
black-box optimisation and QD-Reinforcement-Learning tasks, demonstrating its
benefit across domains. Additionally, our approach outperforms sampling-based
QD methods in uncertain domains when given the same evaluation budget. Overall,
MEMES generates reproducible solutions that are high-performing and diverse
through large-scale ES optimisation on easily accessible hardware.
