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Discovering the underlying mathematical expressions describing a dataset is a core challenge for artificial intelligence.
An introduction to the theory of infinite series
Thomas John I’Anson Bromwich · 1908
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Genetic Programming: On the Programming of Computers by Means of Natural Selection , volume 1
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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Genetic Programming: On the Programming of Computers by Means of Natural Selection , volume 1
John R Koza · 1992
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How to handle constraints with evolutionary algorithms
BGW Craenen, AE Eiben, and E Marchiori · 2001
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How to handle constraints with evolutionary algorithms
BGW Craenen, AE Eiben, and E Marchiori · 2001
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Prefix gene expression programming
Xin Li, Chi Zhou, Weimin Xiao, and Peter C Nelson · 2005
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Distilling free-form natural laws from experimental data
Michael Schmidt and Hod Lipson · 2009
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Distilling free-form natural laws from experimental data
Michael Schmidt and Hod Lipson · 2009
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Semantically-based crossover in genetic programming: application to real-valued symbolic regression
Nguyen Quang Uy, Nguyen Xuan Hoai, Michael O’Neill, Robert I McKay, and Edgar Galván-López · 2011
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Deap: Evolutionary algorithms made easy
Félix-Antoine Fortin, François-Michel De Rainville, Marc-André Gardner, Marc Parizeau, and Christian Gagné · 2012
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Deap: Evolutionary algorithms made easy
Félix-Antoine Fortin, François-Michel De Rainville, Marc-André Gardner, Marc Parizeau, and Christian Gagné · 2012
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Geometric semantic genetic programming
Alberto Moraglio, Krzysztof Krawiec, and Colin G Johnson · 2012
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Practical Methods of Optimization
Roger Fletcher · 2013
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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 · 2013
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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 · 2013
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Policy gradients beyond expectations: Conditional value-at-risk
Aviv Tamar, Yonatan Glassner, and Shie Mannor · 2014
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Policy gradients beyond expectations: Conditional value-at-risk
Aviv Tamar, Yonatan Glassner, and Shie Mannor · 2014
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Automatic formula discovery in the wolfram language
Giorgia Fortuna · 2015
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Human-level control through deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A Rusu, Joel Veness, Marc G Bellemare, Alex Graves, Martin Riedmiller, Andreas K Fidjeland, Georg Ostrovski, et al · 2015
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Automatic formula discovery in the wolfram language
Giorgia Fortuna · 2015
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Improving symbolic regression through a semantics-driven framework
Quang Nhat Huynh, Hemant Kumar Singh, and Tapabrata Ray · 2016
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Using genetic programming with prior formula knowledge to solve symbolic regression problem
Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
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Neural architecture search with reinforcement learning
Barret Zoph and Quoc V Le · 2017
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Grammar variational autoencoder
Matt J Kusner, Brooks Paige, and José Miguel Hernández-Lobato · 2017
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Neural architecture search with reinforcement learning
Barret Zoph and Quoc V Le · 2017
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Neural program synthesis with priority queue training
Daniel A Abolafia, Mohammad Norouzi, Jonathan Shen, Rui Zhao, and Quoc V Le · 2018
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Evolutionary Computation 1: Basic Algorithms and Operators
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Qiang Lu, Jun Ren, and Zhiguang Wang · 2016
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Epopt: Learning robust neural network policies using model ensembles
Aravind Rajeswaran, Sarvjeet Ghotra, Balaraman Ravindran, and Sergey Levine · 2016
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Programming with a differentiable forth interpreter
Sebastian Riedel, Matko Bosnjak, and Tim Rocktäschel · 2016
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neat genetic programming: Controlling bloat naturally
Leonardo Trujillo, Luis Muñoz, Edgar Galván-López, and Sara Silva · 2016
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Improving symbolic regression through a semantics-driven framework
Quang Nhat Huynh, Hemant Kumar Singh, and Tapabrata Ray · 2016
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Geometric semantic genetic programming is overkill
Tomasz P Pawlak · 2016
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neat genetic programming: Controlling bloat naturally
Leonardo Trujillo, Luis Muñoz, Edgar Galván-López, and Sara Silva · 2016
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Thomas Bäck, David B Fogel, and Zbigniew Michalewicz · 2018
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Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor
Tuomas Haarnoja, Aurick Zhou, Pieter Abbeel, and Sergey Levine · 2018
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Memory augmented policy optimization for program synthesis with generalization
Chen Liang, Mohammad Norouzi, Jonathan Berant, Quoc Le, and Ni Lao · 2018
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Learning equations for extrapolation and control
Subham S Sahoo, Christoph H Lampert, and Georg Martius · 2018
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Graphrnn: Generating realistic graphs with deep auto-regressive models
Jiaxuan You, Rex Ying, Xiang Ren, William L Hamilton, and Jure Leskovec · 2018
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Neural program synthesis with priority queue training
Daniel A Abolafia, Mohammad Norouzi, Jonathan Shen, Rui Zhao, and Quoc V Le · 2018
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Learning equations for extrapolation and control
Subham S Sahoo, Christoph H Lampert, and Georg Martius · 2018
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Ying Jin, Weilin Fu, Jian Kang, Jiadong Guo, and Jian Guo · 2019
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Evaluating the search phase of neural architecture search
Kaicheng Yu, Christian Sciuto, Martin Jaggi, Claudiu Musat, and Mathieu Salzmann · 2019
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Ai feynman: A physics-inspired method for symbolic regression
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