- author = "Gabriel Kronberger and Michael Kommenda and Andreas Promberger and Falk Nickel",
- title = "Predicting friction system performance with symbolic regression and genetic programming with factor variables",
- booktitle = "GECCO '18: Proceedings of the Genetic and Evolutionary Computation Conference",
- year = "2018",
- editor = "Hernan Aguirre and Keiki Takadama and Hisashi Handa and Arnaud Liefooghe and Tomohiro Yoshikawa and Andrew M. Sutton and Satoshi Ono and Francisco Chicano and Shinichi Shirakawa and Zdenek Vasicek and Roderich Gross and Andries Engelbrecht and Emma Hart and Sebastian Risi and Ekart Aniko and Julian Togelius and Sebastien Verel and Christian Blum and Will Browne and Yusuke Nojima and Tea Tusar and Qingfu Zhang and Nikolaus Hansen and Jose Antonio Lozano and Dirk Thierens and Tian-Li Yu and Juergen Branke and Yaochu Jin and Sara Silva and Hitoshi Iba and Anna I Esparcia-Alcazar and Thomas Bartz-Beielstein and Federica Sarro and Giuliano Antoniol and Anne Auger and Per Kristian Lehre",
- isbn13 = "978-1-4503-5618-3",
- pages = "1278--1285",
- address = "Kyoto, Japan",
-
DOI = "
doi:10.1145/3205455.3205522",
- publisher = "ACM",
- publisher_address = "New York, NY, USA",
- month = "15-19 " # jul,
- organisation = "SIGEVO",
- keywords = "genetic algorithms, genetic programming",
-
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.",
- notes = "Also known as \cite{3205522} GECCO-2018 A Recombination of the 27th International Conference on Genetic Algorithms (ICGA-2018) and the 23rd Annual Genetic Programming Conference (GP-2018)",
