| Full text | |
| Author(s): |
Haider, C.
;
de Franca, F. O.
;
Burlacu, B.
;
Kronberger, G.
Total Authors: 4
|
| Document type: | Journal article |
| Source: | APPLIED SOFT COMPUTING; v. 132, p. 15-pg., 2023-01-01. |
| Abstract | |
We describe and analyze algorithms for shape-constrained symbolic regression, which allow the inclusion of prior knowledge about the shape of the regression function. This is relevant in many areas of engineering - in particular, when data-driven models, which are based on data of measurements must exhibit certain properties (e.g. positivity, monotonicity, or convexity/concavity). To satisfy these properties, we have extended multi-objective algorithms with shape constraints. A soft-penalty approach is used to minimize both the constraint violations and the prediction error. We use the non -dominated sorting genetic algorithm (NSGA-II) as well as the multi-objective evolutionary algorithm based on decomposition (MOEA/D). The algorithms are tested on a set of models from physics textbooks and compared against previous results achieved with single objective algorithms. Further, we generated out-of-domain samples to test the extrapolation behavior using shape constraints and added a different level of noise on the training data to verify if shape constraints can still help maintain the prediction errors to a minimum and generate valid models. The results showed that the multi-objective algorithms were capable of finding mostly valid models, also when using a soft-penalty approach. Further, we investigated that NSGA-II achieved the best overall ranks on high noise instances.(c) 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). (AU) | |
| FAPESP's process: | 21/12706-1 - Shape-constrained symbolic regression |
| Grantee: | Fabricio Olivetti de França |
| Support Opportunities: | Scholarships abroad - Research |