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Symbolic regression is the process of identifying mathematical expressions that fit observed output from a black-box process.
Importance sampling for stochastic simulations
Peter W Glynn and Donald L Iglehart · 1989
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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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Application of random restart to genetic algorithms
Farzad Ghannadian, Cecil Alford, and Ron Shonkwiler · 1996
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Comparison of genetic algorithms, random restart, and two-opt switching for solving large location-allocation problems
Christopher R. Houck, Jeffrey A. Joines, and Michael G. Kay · 1996
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Using fitness distributions to improve the evolution of learning structures
Christian Igel and Martin Kreutz · 1999
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Faster genetic programming based on local gradient search of numeric leaf values
Alexander Topchy and William Punch · 2001
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Evolving neural networks through augmenting topologies
Kenneth O. Stanley and Risto Miikkulainen · 2002
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Use of genetic algorithms and gradient based optimization techniques for calcium phosphate precipitation
Ludovic Montastruc, Catherine Azzaro-Pantel, Luc Pibouleau, and Serge Domenech · 2003
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Genetic programming with gradient descent search for multiclass object classification
Mengjie Zhang and Will Smart · 2004
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A tutorial on the cross-entropy method
Pieter-Tjerk De Boer, Dirk P Kroese, Shie Mannor, and Reuven Y Rubinstein · 2005
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Neuroevolution: from architectures to learning
Dario Floreano, Peter Dürr, and Claudio Mattiussi · 2008
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Natural evolution strategies
Daan Wierstra, Tom Schaul, Jan Peters, and Juergen Schmidhuber · 2008
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Distilling free-form natural laws from experimental data
Michael Schmidt and Hod Lipson · 2009
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Restart scheduling for genetic algorithms
Alex S. Fukunaga · 2010
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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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Approximating geometric crossover by semantic backpropagation
Krzysztof Krawiec and Tomasz Pawlak · 2013
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Semantically-based crossover in genetic programming: Application to realvalued symbolic regression
Nguyen Quang Uy, Nguyen Xuan Hoai, Michael O’Neill, R.I. McKay, and Edgar Galvan-Lopez · 2014
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Neural combinatorial optimization with reinforcement learning
Irwan Bello, Hieu Pham, Quoc V Le, Mohammad Norouzi, and Samy Bengio · 2016
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Continuous control with deep reinforcement learning
Timothy P Lillicrap, Jonathan J Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, and Daan Wierstra · 2016
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Reinforcement learning’s foundational flaw
Andrey Kurenkov · 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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Felipe Petroski Such, Vashisht Madhavan, Edoardo Conti, Joel Lehman, Kenneth O. Stanley, and Jeff Clune · 2018
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Renas: Reinforced evolutionary neural architecture search
Yukang Chen, Gaofeng Meng, Qian Zhang, Shiming Xiang, Chang Huang, Lisen Mu, and Xinggang Wang · 2019
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Ying Jin, Weilin Fu, Jian Kang, Jiadong Guo, and Jian Guo · 2019
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Using genetic programming with prior formula knowledge to solve symbolic regression problem
Qiang Lu, Jun Ren, and Zhiguang Wang · 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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Grammar variational autoencoder
Matt J Kusner, Brooks Paige, and José Miguel Hernández-Lobato · 2017
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Continual and one-shot learning through neural networks with dynamic external memory
Benno Lüders, Mikkel Schläger, Aleksandra Korach, and Sebastian Risi · 2017
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Neuroevolution in games: State of the art and open challenges
Sebastian Risi and Julian Togelius · 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
Thomas Bäck, David B Fogel, and Zbigniew Michalewicz · 2018
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Evolving deep neural networks
Risto Miikkulainen1, Jason Liang, Elliot Meyerson, Aditya Rawal, Dan Fink, OlivierFrancon, Bala Raju, Hormoz Shahrzad, Arshak Navruzyan, Nigel Duffy, and Babak Hodjat · 2019
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Cem-rl: Combining evolutionary and gradient-based methods for policy search
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Automl-zero: Evolving machine learning algorithms from scratch
Esteban Real, Chen Liang, David R. So, and Quoc V. Leg · 2019
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Deep reinforcement learning using genetic algorithm for parameter optimization
Adarsh Sehgal, Hung La, Sushil Louis, and Hai Nguyen · 2019
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Designing neural networks through neuroevolution
Kenneth O. Stanley, Jeff Clune, Joel Lehman1, and Risto Miikkulainen · 2019
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Guiding deep molecular optimization with genetic exploration
Sungsoo Ahn, Junsu Kim, Hankook Lee, and Jinwoo Shin · 2020
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Off-policy reinforcement learning for efficient and effective gan architecture search
Yuan Tian, Qin Wang1, Zhiwu Huang, Wen Li, Dengxin Dai, Minghao Yang, Jun Wang, , and Olga Fink · 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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Deep symbolic regression: Recovering mathematical expressions from data via risk-seeking policy gradients
Brenden K Petersen, Mikel Landajuela, T Nathan Mundhenk, Claudio P Santiago, Soo K Kim, and Joanne T Kim · 2021
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