  In Quality-Diversity (QD) algorithms, which evolve a behaviourally diverse
archive of high-performing solutions, the behaviour space is a difficult design
choice that should be tailored to the target application. In QD meta-evolution,
one evolves a population of QD algorithms to optimise the behaviour space based
on an archive-level objective, the meta-fitness. This paper proposes an
improved meta-evolution system such that (i) the database used to rapidly
populate new archives is reformulated to prevent loss of quality-diversity;
(ii) the linear transformation of base-features is generalised to a
feature-map, a function of the base-features parametrised by the meta-genotype;
and (iii) the mutation rate of the QD algorithm and the number of generations
per meta-generation are controlled dynamically. Experiments on an 8-joint
planar robot arm compare feature-maps (linear, non-linear, and
feature-selection), parameter control strategies (static, endogenous,
reinforcement learning, and annealing), and traditional MAP-Elites variants,
for a total of 49 experimental conditions. Results reveal that non-linear and
feature-selection feature-maps yield a 15-fold and 3-fold improvement in
meta-fitness, respectively, over linear feature-maps. Reinforcement learning
ranks among top parameter control methods. Finally, our approach allows the
robot arm to recover a reach of over 80% for most damages and at least 60% for
severe damages.
