Optuna: A Next-generation Hyperparameter Optimization Framework

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 opportunity of our approach is the ability to detect transitions between different continuous behaviors of a system directly based on the dynamics. From a diagnosis perspective, this can advantageously be used to detect the system entering fault modes and identify their models. This paper presents a parameter study for a symbolic regression based identification algorithm.

Cite this paper

@misc{plambeck2019optuna,
  title = {{Optuna: A Next-generation Hyperparameter Optimization Framework}},
  author = {Plambeck, Swantje and Schmidt, Maximilian and Subias, Audine and Travé-Massuyès, Louise and Fey, Goerschwin},
  journal = {arXiv (Cornell University)},
  year = {2019},
  doi = {10.4230/oasics.dx.2024.30}
}

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