  By combining Genetic Programming, MAP-Elites and Covariance Matrix Adaptation
Evolution Strategy, we demonstrate very high success rates in Symbolic
Regression problems. MAP-Elites is used to improve exploration while preserving
diversity and avoiding premature convergence and bloat. Then, a Covariance
Matrix Adaptation-Evolution Strategy is used to evaluate free scalars through a
non-gradient-based black-box optimizer. Although this evaluation approach is
not computationally scalable to high dimensional problems, our algorithm is
able to find exactly most of the $31$ targets extracted from the literature on
which we evaluate it.
