1 research outputs found
Exploration and Exploitation in Symbolic Regression using Quality-Diversity and Evolutionary Strategies Algorithms
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 targets extracted from the literature on
which we evaluate it.Comment: 11 pages, 7 figures, 5 table