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

被引:5
作者
Kronberger, Gabriel [1 ]
Kommenda, Michael [1 ]
Promberger, Andreas [2 ]
Nickel, Falk [2 ]
机构
[1] Univ Appl Sci Upper Austria, Sch Informat Commun & Media, Hagenberg, Austria
[2] Miba Frictec GmbH, A-4661 Roitham, Austria
来源
GECCO'18: PROCEEDINGS OF THE 2018 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE | 2018年
关键词
Symbolic Regression; Friction Systems; Prediction;
D O I
10.1145/3205455.3205522
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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.
引用
收藏
页码:1278 / 1285
页数:8
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