# Predicting Friction System Performance with Symbolic Regression and Genetic Programming with Factor Variables

arXiv: https://arxiv.org/abs/2107.09484 · HTML: https://mlsanity.com/paper/2107.09484/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 15 references that are in the map.
- First posted on arXiv 19 Jul 2021.

## Built on by (most cited first, 7 of 7)

- [Symbolic Regression is NP-hard](https://mlsanity.com/paper/2207.01018/) (2022)
- [A Comparison of Recent Algorithms for Symbolic Regression to Genetic Programming](https://mlsanity.com/paper/2406.03585/) (2024)
- [Ab Initio Nonparametric Variable Selection for Scalable Symbolic Regression with Large p](https://mlsanity.com/paper/2410.13681/) (2024)
- [A Functional Analysis Approach to Symbolic Regression](https://mlsanity.com/paper/2402.06299/) (2024)
- [Symbolic Quantile Regression for the Interpretable Prediction of Conditional Quantiles](https://mlsanity.com/paper/2508.08080/) (2025)
- [Exploring Multi-view Symbolic Regression methods in physical sciences](https://mlsanity.com/paper/2509.10500/) (2025)
- [Symbolic Regression for Shared Expressions: Introducing Partial Parameter Sharing](https://mlsanity.com/paper/2601.04051/) (2026)

## Abstract (arXiv)

Friction systems are mechanical systems wherein friction is used for force transmission (e.g. mechanical braking systems or automatic gearboxes). For finding optimal and safe design parameters, engineers have to predict friction system performance. This is especially difficult in real-world applications, because it is affected by many parameters. We have used symbolic regression and genetic programming for finding accurate and trustworthy prediction models for this task. However, it is not straight-forward how nominal variables can be included. In particular, a one-hot-encoding is unsatisfactory because genetic programming tends to remove such indicator variables. We have therefore used so-called factor variables for representing nominal variables in symbolic regression models. Our results show that GP is able to produce symbolic regression models for predicting friction performance with predictive accuracy that is comparable to artificial neural networks. The symbolic regression models with factor variables are less complex than models using a one-hot encoding.