  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.
