Symbolic regression driven by training data and prior knowledge

Symbolic regression driven by training data and prior knowledge

Kubalik, Jiai (Czech Technical University)
Derner, Erik (Czech Technical University)
Babuska, R. (TU Delft Learning & Autonomous Control; Czech Technical University)

2020

In symbolic regression, the search for analytic models is typically driven purely by the prediction error observed on the training data samples. However, when the data samples do not sufficiently cover the input space, the prediction error does not provide sufficient guidance toward desired models. Standard symbolic regression techniques then yield models that are partially incorrect, for instance, in terms of their steady-state characteristics or local behavior. If these properties were considered already during the search process, more accurate and relevant models could be produced. We propose a multi-objective symbolic regression approach that is driven by both the training data and the prior knowledge of the properties the desired model should manifest. The properties given in the form of formal constraints are internally represented by a set of discrete data samples on which candidate models are exactly checked. The proposed approach was experimentally evaluated on three test problems with results clearly demonstrating its capability to evolve realistic models that fit the training data well while complying with the prior knowledge of the desired model characteristics at the same time. It outperforms standard symbolic regression by several orders of magnitude in terms of the mean squared deviation from a reference model.

Genetic programming
Model learning
Multi-objective optimization
Symbolic regression

http://resolver.tudelft.nl/uuid:0d2ec01c-47a0-4667-857e-4cf13d0a67fe

https://doi.org/10.1145/3377930.3390152

Association for Computing Machinery (ACM), New York, NY, USA

978-1-4503-7128-5

Proceedings of the 2020 Genetic and Evolutionary Computation Conference, GECCO 2020

2020 Genetic and Evolutionary Computation Conference, GECCO 2020, 2020-07-08 → 2020-07-12, Cancun, Mexico

Institutional Repository

conference paper

© 2020 Jiai Kubalik, Erik Derner, R. Babuska