  Differential MAP-Elites is a novel algorithm that combines the illumination
capacity of CVT-MAP-Elites with the continuous-space optimization capacity of
Differential Evolution. The algorithm is motivated by observations that
illumination algorithms, and quality-diversity algorithms in general, offer
qualitatively new capabilities and applications for evolutionary computation
yet are in their original versions relatively unsophisticated optimizers. The
basic Differential MAP-Elites algorithm, introduced for the first time here, is
relatively simple in that it simply combines the operators from Differential
Evolution with the map structure of CVT-MAP-Elites. Experiments based on 25
numerical optimization problems suggest that Differential MAP-Elites clearly
outperforms CVT-MAP-Elites, finding better-quality and more diverse solutions.

Neural and Evolutionary Computing