Optuna: A Next-generation Hyperparameter Optimization Framework
Universität Hamburg · Hamburg University of Technology · +4 more institutions
Abstract
Hybrid systems, which combine both continuous and discrete behavior, are used in many fields, including robotics, biological systems, and control systems. However, due to their complexity, finding an accurate model is a challenge. This paper discusses the usage of symbolic regression to learn hybrid systems from data and specifically analyses learning parameters for a recent algorithm. Symbolic regression is a powerful tool that can automatically discover accurate and interpretable mathematical models in the form of symbolic expressions. Models generated by symbolic regression are a valuable tool for system identification and diagnosis, e.g., to predict future system behavior or detect anomalies. A major…
Citation impact
- FWCI
- 39.75
- Percentile
- 100%
- References
- 23
Authors
5- PSPlambeck, SwantjeCorresponding
Universität Hamburg, Hamburg University of Technology
- SMSchmidt, Maximilian
Universität Hamburg, Hamburg University of Technology
- SASubias, Audine
Centre National de la Recherche Scientifique, Université Fédérale de Toulouse Midi-Pyrénées, Laboratoire d'Analyse et d'Architecture des Systèmes, Institut National des Sciences Appliquées de Toulouse
- TLTravé-Massuyès, Louise
Centre National de la Recherche Scientifique, Université Fédérale de Toulouse Midi-Pyrénées, Laboratoire d'Analyse et d'Architecture des Systèmes
- FGFey, Goerschwin
Universität Hamburg, Hamburg University of Technology
Topics & keywords
- Computer science
- Pruning
- Software
- Hyperparameter
- Software development
- Scalability
- Resource-oriented architecture
- Point (geometry)
- Industry, innovation and infrastructure