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Many promising approaches to symbolic regression have been presented in recent years, yet progress in the field continues to suffer from a lack of uniform, robust, and transparent benchmarking standards.
Genetic Programming: On the Programming of Computers by Means of Natural Selection
John R. Koza · 1992
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Simple statistical gradient-following algorithms for connectionist reinforcement learning
Ronald J. Williams · 1992
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A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimization: NSGA-II
Kalyanmoy Deb, Samir Agrawal, Amrit Pratap, and T Meyarivan · 2000
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SPEA2: Improving the Strength Pareto Evolutionary Algorithm
Eckart Zitzler, Marco Laumanns, and Lothar Thiele · 2001
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Multiobjective genetic programming: Reducing bloat using SPEA2
S. Bleuler, M. Brack, L. Thiele, and E. Zitzler · 2001
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Faster genetic programming based on local gradient search of numeric leaf values
Alexander Topchy and William F. Punch · 2001
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Leo Breiman · 2001
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Greedy function approximation: A gradient boosting machine
Jerome H Friedman · 2001
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The boosting approach to machine learning: An overview
Robert E. Schapire · 2003
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Pareto-front exploitation in symbolic regression
Guido F. Smits and Mark Kotanchek · 2005
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Nonlinear System Identification Using Coevolution of Models and Tests
J.C. Bongard and H. Lipson · 2005
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Continuous Integration
Martin Fowler · 2006
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ALPS: The age-layered population structure for reducing the problem of premature convergence
Gregory S. Hornby · 2006
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Statistical Comparisons of Classifiers over Multiple Data Sets
Janez Demšar · 2006
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Comparison of Tree and Graph Encodings As Function of Problem Complexity
Michael Schmidt and Hod Lipson · 2007
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Coevolution of Fitness Predictors
M.D. Schmidt and H. Lipson · 2008
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Order of Nonlinearity as a Complexity Measure for Models Generated by Symbolic Regression via Pareto Genetic Programming
E.J. Vladislavleva, G.F. Smits, and D. den Hertog · 2008
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Machine Science: Automated Modeling of Deterministic and Stochastic Dynamical Systems
Michael Douglas Schmidt · 2011
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Scikit-learn: Machine learning in Python
Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, et al · 2011
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FFX: Fast, scalable, deterministic symbolic regression technology
Trent McConaghy · 2011
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Automated refinement and inference of analytical models for metabolic networks
Michael D Schmidt, Ravishankar R Vallabhajosyula, Jerry W Jenkins, Jonathan E Hood, Abhishek S Soni, John P Wikswo, and Hod Lipson · 2011
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Modeling global temperature changes with genetic programming
Karolina Stanislawska, Krzysztof Krawiec, and Zbigniew W. Kundzewicz · 2012
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Genetic Algorithms and Genetic Programming in Computational Finance
Shu-Heng Chen · 2012
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Genetic programming needs better benchmarks
James McDermott, David R. White, Sean Luke, Luca Manzoni, Mauro Castelli, Leonardo Vanneschi, Wojciech Jaskowski, Krzysztof Krawiec, Robin Harper, and Kenneth De Jong · 2012
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Better GP benchmarks: Community survey results and proposals
David R. White, James McDermott, Mauro Castelli, Luca Manzoni, Brian W. Goldman, Gabriel Kronberger, Wojciech Jaśkowski, Una-May O’Reilly, and Sean Luke · 2012
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Running programs backwards: Instruction inversion for effective search in semantic spaces
Bartosz Wieloch and Krzysztof Krawiec · 2013
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Approximating geometric crossover by semantic backpropagation
Krzysztof Krawiec and Tomasz Pawlak · 2013
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OpenML: Networked Science in Machine Learning
Joaquin Vanschoren, Jan N. van Rijn, Bernd Bischl, and Luis Torgo · 2013
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UCI Machine Learning Repository
The Mythos of Model Interpretability: In machine learning, the concept of interpretability is both important and slippery
Zachary C Lipton · 2018
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Where are we now? A large benchmark study of recent symbolic regression methods
Patryk Orzechowski, William La Cava, and Jason H. Moore · 2018
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A greedy search tree heuristic for symbolic regression
Fabrício Olivetti de França · 2018
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Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness
Michael Kearns, Seth Neel, Aaron Roth, and Zhiwei Steven Wu · 2018
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The global landscape of AI ethics guidelines
Anna Jobin, Marcello Ienca, and Effy Vayena · 2019
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M. Lichman · 2013
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Multiple regression genetic programming
Ignacio Arnaldo, Krzysztof Krawiec, and Una-May O’Reilly · 2014
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Semantic backpropagation for designing search operators in genetic programming
Tomasz P Pawlak, Bartosz Wieloch, and Krzysztof Krawiec · 2014
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Nonlinear Dynamics and Chaos: With Applications to Physics, Biology, Chemistry, and Engineering
Steven H Strogatz · 2014
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Automatic identification of wind turbine models using evolutionary multiobjective optimization
William La Cava, Kourosh Danai, Lee Spector, Paul Fleming, Alan Wright, and Matthew Lackner · 2015
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A C++ framework for geometric semantic genetic programming
Mauro Castelli, Sara Silva, and Leonardo Vanneschi · 2015
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Automatic Formula Discovery in the Wolfram Language – from Wolfram Library Archive
Giorgia Fortuna · 2015
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Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
Cynthia Rudin · 2019
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Parameter identification for symbolic regression using nonlinear least squares
Michael Kommenda, Bogdan Burlacu, Gabriel Kronberger, and Michael Affenzeller · 2019
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Linear scaling with and within semantic backpropagation-based genetic programming for symbolic regression
Marco Virgolin, Tanja Alderliesten, and Peter AN Bosman · 2019
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Definitions, methods, and applications in interpretable machine learning
W. James Murdoch, Chandan Singh, Karl Kumbier, Reza Abbasi-Asl, and Bin Yu · 2019
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Application of concise machine learning to construct accurate and interpretable EHR computable phenotypes
William La Cava, Paul C. Lee, Imran Ajmal, Xiruo Ding, Priyanka Solanki, Jordana B. Cohen, Jason H. Moore, and Daniel S. Herman · 2020
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Ying Jin, Weilin Fu, Jian Kang, Jiadong Guo, and Jian Guo · 2020
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Deep symbolic regression: Recovering mathematical expressions from data via risk-seeking policy gradients
Brenden K. Petersen, Mikel Landajuela Larma, Terrell N. Mundhenk, Claudio Prata Santiago, Soo Kyung Kim, and Joanne Taery Kim · 2020
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AI Feynman: A Physics-Inspired Method for Symbolic Regression
Silviu-Marian Udrescu and Max Tegmark · 2020
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AI Feynman 2.0: Pareto-optimal symbolic regression exploiting graph modularity
Silviu-Marian Udrescu, Andrew Tan, Jiahai Feng, Orisvaldo Neto, Tailin Wu, and Max Tegmark · 2020
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Benchmarking state-of-the-art symbolic regression algorithms
Jan Žegklitz and Petr Pošík · 2020
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Interaction-Transformation Evolutionary Algorithm for Symbolic Regression
F. O. de Franca and G. S. I. Aldeia · 2020
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Operon C++ an efficient genetic programming framework for symbolic regression
Bogdan Burlacu, Gabriel Kronberger, and Michael Kommenda · 2020
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Feature standardisation and coefficient optimisation for effective symbolic regression
Grant Dick, Caitlin A. Owen, and Peter A. Whigham · 2020
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Genetic programming approaches to learning fair classifiers
William La Cava and Jason H. Moore · 2020
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Automated Knowledge Discovery Using Neural Networks
Maysum Panju · 2021
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Matthias Werner, Andrej Junginger, Philipp Hennig, and Georg Martius · 2021
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Forough Poursabzi-Sangdeh, Daniel G Goldstein, Jake M Hofman, Jennifer Wortman Wortman Vaughan, and Hanna Wallach · 2021
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Joseph D. Romano, Trang T. Le, William La Cava, John T. Gregg, Daniel J. Goldberg, Natasha L. Ray, Praneel Chakraborty, Daniel Himmelstein, Weixuan Fu, and Jason H. Moore · 2021
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