# Smooth Symbolic Regression: Transformation of Symbolic Regression into a Real-valued Optimization Problem

arXiv: https://arxiv.org/abs/2108.03274 · HTML: https://mlsanity.com/paper/2108.03274/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 25 references that are in the map.
- First posted on arXiv 6 Aug 2021.

## Built on by (most cited first, 1 of 1)

- [Accelerating Understanding of Scientific Experiments with End to End Symbolic Regression](https://mlsanity.com/paper/2112.04023/) (2021)

## Abstract (arXiv)

The typical methods for symbolic regression produce rather abrupt changes in solution candidates. In this work, we have tried to transform symbolic regression from an optimization problem, with a landscape that is so rugged that typical analysis methods do not produce meaningful results, to one that can be compared to typical and very smooth real-valued problems. While the ruggedness might not interfere with the performance of optimization, it restricts the possibilities of analysis. Here, we have explored different aspects of a transformation and propose a simple procedure to create real-valued optimization problems from symbolic regression problems.