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By combining Genetic Programming, MAP-Elites and Covariance Matrix AdaptationEvolution Strategy, we demonstrate very high success rates in SymbolicRegression problems. MAP-Elites is used to improve exploration while preservingdiversity and avoiding premature convergence and bloat. Then, a CovarianceMatrix Adaptation-Evolution Strategy is used to evaluate free scalars through anon-gradient-based black-box optimizer. Although this evaluation approach isnot computationally scalable to high dimensional problems, our algorithm isable to find exactly most of the $31$ targets extracted from the literature onwhich we evaluate it.

Exploration and Exploitation in Symbolic Regression using Quality-Diversity and Evolutionary Strategies Algorithms