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

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@InProceedings{Kronberger:2018:GECCO,
  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)",
}

Genetic Programming entries for Gabriel Kronberger Michael Kommenda Andreas Promberger Falk Nickel

Citations