Predicting friction system performance with symbolic regression and genetic programming with factor variables

Gabriel Kronberger, Michael Kommenda, Andreas Promberger, Falk Nickel

Research output: Chapter in Book/Report/Conference proceedingsConference contributionpeer-review

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 languageEnglish
Title of host publicationGECCO 2018 - Proceedings of the 2018 Genetic and Evolutionary Computation Conference
PublisherACM Press
Pages1278-1285
Number of pages8
ISBN (Electronic)9781450356183
DOIs
Publication statusPublished - 2 Jul 2018
EventGenetic and Evolutionary Computation Conference (GECCO 2018) - Kyoto, Japan, Japan
Duration: 15 Jul 201819 Jul 2018
http://gecco-2018.sigevo.org/

Publication series

NameGECCO 2018 - Proceedings of the 2018 Genetic and Evolutionary Computation Conference

Conference

ConferenceGenetic and Evolutionary Computation Conference (GECCO 2018)
CountryJapan
CityKyoto, Japan
Period15.07.201819.07.2018
Internet address

Keywords

  • Friction Systems
  • Prediction
  • Symbolic Regression

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