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

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