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Symbolic regression (SR) is the task of learning a model of data in the form of a mathematical expression.
Information processing, data inferences, and scientific generalization
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Application of the genetic algorithm to automatic program generation
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Strongly typed genetic programming
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Selection based on the pareto nondomination criterion for controlling code growth in genetic programming
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Grammatical evolution
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Introduction to NP-Completeness of knapsack problems
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Analytic programming–symbolic regression by means of arbitrary evolutionary algorithms
I. Zelinka, Z. Oplatkova, and L. Nolle · 2005
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Regularization and variable selection via the elastic net
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The runge phenomenon and spatially variable shape parameters in rbf interpolation
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A Field Guide to Genetic Programming
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Grammar-based genetic programming: a survey
R. I. McKay, N. X. Hoai, P. A. Whigham, Y. Shan, and M. O’neill · 2010
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FFX: Fast, scalable, deterministic symbolic regression technology
T. McConaghy · 2011
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Safe and interpretable machine learning: A methodological review
C. Otte · 2013
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Prioritized grammar enumeration: Symbolic regression by dynamic programming
T. Worm and K. Chiu · 2013
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Data-and theory-driven techniques for surrogate-based optimization
A. Cozad · 2014
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Using genetic programming with prior formula knowledge to solve symbolic regression problem
Q. Lu, J. Ren, and Z. Wang · 2016
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Constructing parsimonious analytic models for dynamic systems via symbolic regression
E. Derner, J. Kubalík, N. Ancona, and R. Babuška · 2020
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Symbolic Regression by Exhaustive Search: Reducing the Search Space Using Syntactical Constraints and Efficient Semantic Structure Deduplication , pages 79–99
L. Kammerer, G. Kronberger, B. Burlacu, S. M. Winkler, M. Kommenda, and M. Affenzeller · 2020
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Deep symbolic regression: Recovering mathematical expressions from data via risk-seeking policy gradients
B. K. Petersen, M. L. Larma, T. N. Mundhenk, C. P. Santiago, S. K. Kim, and J. T. Kim · 2020
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AI Feynman: A physics-inspired method for symbolic regression
S.-M. Udrescu and M. Tegmark · 2020
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Artificial intelligence act, 2021
European Commission · 2021
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A global MINLP approach to symbolic regression
A. Cozad and N. V. Sahinidis · 2018
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Interpretable policies for reinforcement learning by genetic programming
D. Hein, S. Udluft, and T. A. Runkler · 2018
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Predicting friction system performance with symbolic regression and genetic programming with factor variables
G. Kronberger, M. Kommenda, A. Promberger, and F. Nickel · 2018
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Learning concise representations for regression by evolving networks of trees
W. La Cava, T. R. Singh, J. Taggart, S. Suri, and J. H. Moore · 2018
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A greedy search tree heuristic for symbolic regression
F. Olivetti de França · 2018
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Fast, accurate, and transferable many-body interatomic potentials by symbolic regression
A. Hernandez, A. Balasubramanian, F. Yuan, S. A. Mason, and T. Mueller · 2019
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Contemporary symbolic regression methods and their relative performance
W. La Cava, P. Orzechowski, B. Burlacu, F. O. de Franca, M. Virgolin, Y. Jin, M. Kommenda, and J. H. Moore · 2021
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Algorithmic accountability act, 2022
117th US Congress · 2022
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Deep symbolic regression for recurrent sequences
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End-to-end symbolic regression with transformers
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