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Shape-constrained Symbolic Regression – Improving Extrapolation with Prior Knowledge
2021
Evolutionary Computation
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 e.g. monotonicity of the function over selected inputs. The aim is to find models which conform to expected behaviour and which have improved extrapolation capabilities. We demonstrate the feasibility of the idea and propose and compare two evolutionary
doi:10.1162/evco_a_00294
pmid:34623432
fatcat:gwyid3r3mbesxds5wmwy3hh4fm