## Abstract

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-worlds 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.

Original language | English |
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Title of host publication | GECCO 2018 - Proceedings of the 2018 Genetic and Evolutionary Computation Conference |

Publisher | ACM Press |

Pages | 1278-1285 |

Number of pages | 8 |

ISBN (Electronic) | 9781450356183 |

DOIs | |

Publication status | Published - 2 Jul 2018 |

Event | Genetic and Evolutionary Computation Conference (GECCO 2018) - Kyoto, Japan, Japan Duration: 15 Jul 2018 → 19 Jul 2018 http://gecco-2018.sigevo.org/ |

### Publication series

Name | GECCO 2018 - Proceedings of the 2018 Genetic and Evolutionary Computation Conference |
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### Conference

Conference | Genetic and Evolutionary Computation Conference (GECCO 2018) |
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Country/Territory | Japan |

City | Kyoto, Japan |

Period | 15.07.2018 → 19.07.2018 |

Internet address |

## Keywords

- Friction Systems
- Prediction
- Symbolic Regression