Results 11 to 20 of about 6,391,262 (321)
Genetic Programming and Evolvable Machines, 2021 Modifying standard gradient boosting by replacing the embedded weak learner in favor of a strong(er) one, we present SyRBo: Symbolic-Regression Boosting. Experiments over 98 regression datasets show that by adding a small number of boosting stages -- between 2--5 -- to a symbolic regressor, statistically significant improvements can often be attained ...
Moshe Sipper, Jason H. Moore
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Vertical Symbolic Regression [PDF]
CoRR, 2023 Automating scientific discovery has been a grand goal of Artificial Intelligence (AI) and will bring tremendous societal impact. Learning symbolic expressions from experimental data is a vital step in AI-driven scientific discovery. Despite exciting progress, most endeavors have focused on the horizontal discovery paths, i.e., they directly search for ...
Nan Jiang 0012, Md. Nasim, Yexiang Xue
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Deep Generative Symbolic Regression [PDF]
CoRR, 2023 In the proceedings of the Eleventh International Conference on Learning Representations (ICLR 2023).
Samuel Holt +2 more
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Controllable Neural Symbolic Regression
CoRR, 2023 In symbolic regression, the goal is to find an analytical expression that accurately fits experimental data with the minimal use of mathematical symbols such as operators, variables, and constants. However, the combinatorial space of possible expressions can make it challenging for traditional evolutionary algorithms to find the correct expression in a
Bendinelli, Tommaso +2 more
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SRBench++ : principled benchmarking of symbolic regression with domain-expert interpretation. [PDF]
IEEE Trans Evol ComputSymbolic regression (SR) searches for analytic expressions that accurately describe studied phenomena. The main promise of this approach is that it may return an interpretable model that can be insightful to users, while maintaining high accuracy.
de Franca FO +23 more
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Glyph: Symbolic Regression Tools
Journal of Open Research Software, 2019 We present Glyph – a Python package for genetic programming based symbolic regression. Glyph is designed for usage in numerical simulations as well as real world experiments.
Markus Quade, Julien Gout, Markus Abel
doaj +6 more sources
Automated data-driven discovery of material models based on symbolic regression: A case study on the human brain cortex. [PDF]
Acta BiomaterWe introduce a data-driven framework to automatically identify interpretable and physically meaningful hyperelastic constitutive models from sparse data.
Hou J, Chen X, Wu T, Kuhl E, Wang X.
europepmc +3 more sources
Recent Advances in Symbolic Regression
ACM Computing SurveysSymbolic regression (SR) is an optimization problem that identifies the most suitable mathematical expression or model to fit the observed dataset.
Junlan Dong, Jinghui Zhong
semanticscholar +2 more sources
Dimension Reduction for Symbolic Regression
Proceedings of the AAAI Conference on Artificial IntelligenceSolutions of symbolic regression problems are expressions that are composed of input variables and operators from a finite set of function symbols. One measure for evaluating symbolic regression algorithms is their ability to recover formulae, up to symbolic equivalence, from finite samples.
Paul Kahlmeyer +2 more
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STM-based symbolic regression for strength prediction of RC deep beams and corbels. [PDF]
Sci RepThis study uses symbolic regression with a strut-and-tie model to predict the shear strength of reinforced concrete deep beams (RCDBs) and corbels (RCCs).
Megahed K.
europepmc +2 more sources