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

arXiv: https://arxiv.org/abs/1906.03959 · HTML: https://mlsanity.com/paper/1906.03959/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jun 2019.

## Built on by (most cited first, 2 of 2)

- [Quality Diversity under Sparse Reward and Sparse Interaction: Application to Grasping in Robotics](https://mlsanity.com/paper/2308.05483/) (2023)
- [An Interpretable Approach to PDE Solution Discovery via Structural Experience Distillation](https://mlsanity.com/paper/2610.12003/) (2026)

## Abstract (arXiv)

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.