| Full text | |
| Author(s): |
Total Authors: 5
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| Affiliation: | [1] Univ Appl Sci Upper Austria, Josef Ressel Ctr Symbol Regress, Softwarepk 11, A-4232 Hagenberg - Austria
[2] Fed Univ ABC, Ctr Math Computat & Cognit CMCC, Heurist Anal & Learning Lab HAL, Santo Andre, SP - Brazil
Total Affiliations: 2
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| Document type: | Journal article |
| Source: | EVOLUTIONARY COMPUTATION; v. 30, n. 1, p. 75-98, MAR 1 2022. |
| Web of Science Citations: | 0 |
| Abstract | |
We investigate the addition of constraints on the function image and its derivatives for the incorporation of prior knowledge in symbolic regression. The approach is called shape-constrained symbolic regression and allows us to enforce, for example, monotonicity of the function over selected inputs. The aim is to find models which conform to expected behavior and which have improved extrapolation capabilities. We demonstrate the feasibility of the idea and propose and compare two evolutionary algorithms for shape-constrained symbolic regression: (i) an extension of tree-based genetic programming which discards infeasible solutions in the selection step, and (ii) a two-population evolutionary algorithm that separates the feasible from the infeasible solutions. In both algorithms we use interval arithmetic to approximate bounds for models and their partial derivatives. The algorithms are tested on a set of 19 synthetic and four real-world regression problems. Both algorithms are able to identify models which conform to shape constraints which is not the case for the unmodified symbolic regression algorithms. However, the predictive accuracy of models with constraints is worse on the training set and the test set. Shape-constrained polynomial regression produces the best results for the test set but also significantly larger models. (AU) | |
| FAPESP's process: | 18/14173-8 - Generalization and Applications of Interaction-Transformation Algebraic Data Type |
| Grantee: | Fabricio Olivetti de França |
| Support Opportunities: | Regular Research Grants |