  We propose Multi-Task Multi-Behavior MAP-Elites, a variant of MAP-Elites that
finds a large number of high-quality solutions for a large set of tasks
(optimization problems from a given family). It combines the original
MAP-Elites for the search for diversity and Multi-Task MAP-Elites for
leveraging similarity between tasks. It performs better than three baselines on
a humanoid fault-recovery set of tasks, solving more tasks and finding twice as
many solutions per solved task.
