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Shape-constrained Symbolic Regression with NSGA-III
Both algorithms are capable of dealing with multiple objectives, whereas NSGA-II is a well established multi-objective approach performing well on instances with up-to 3 objectives.
Using Shape Constraints for Improving Symbolic Regression Models
We describe and analyze algorithms for shape-constrained symbolic regression, which allows the inclusion of prior knowledge about the shape of the regression function.
Shape-constrained Symbolic Regression -- Improving Extrapolation with Prior Knowledge
Both algorithms are able to identify models which conform to shape constraints which is not the case for the unmodified symbolic regression algorithms.
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