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https://github.com/Alro10/awesome-deep-neuroevolution

A collection of Deep Neuroevolution resources or evolutionary algorithms applying in Deep Learning (constantly updating)
https://github.com/Alro10/awesome-deep-neuroevolution
List: awesome-deep-neuroevolution
awesome deep-neural-networks deep-neuroevolution deep-reinforcement-learning evolution-strategies evolutionary-algorithms genetic-algorithms neuroevolution
Last synced: about 2 months ago
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A collection of Deep Neuroevolution resources or evolutionary algorithms applying in Deep Learning (constantly updating)
Host: GitHub
URL: https://github.com/Alro10/awesome-deep-neuroevolution
Owner: Alro10
License: apache-2.0
Created: 2019-02-15T17:44:23.000Z (over 5 years ago)
Default Branch: master
Last Pushed: 2021-04-20T12:53:18.000Z (over 3 years ago)
Last Synced: 2024-07-29T17:14:21.058Z (about 2 months ago)
Topics: awesome, deep-neural-networks, deep-neuroevolution, deep-reinforcement-learning, evolution-strategies, evolutionary-algorithms, genetic-algorithms, neuroevolution
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Stars: 219
Watchers: 19
Forks: 35
Open Issues: 1
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project

ultimate-awesome - awesome-deep-neuroevolution - A collection of Deep Neuroevolution resources or evolutionary algorithms applying in Deep Learning (constantly updating). (Other Lists / PowerShell Lists)
README

        # Awesome Deep Neuroevolution

[![Awesome](https://awesome.re/badge.svg)](https://awesome.re)

[![PRsWelcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square)](http://makeapullrequest.com)







A collection of Deep Neuroevolution and evolutionary computation resources. Inspired by [awesome-deep-learning-papers](https://github.com/terryum/awesome-deep-learning-papers), [awesome-meta-learning](https://github.com/dragen1860/awesome-meta-learning), the early paper by [OpenAI-Evolution Strategies](https://arxiv.org/abs/1703.03864) and the amazing work from **Uber AI Labs** [blog](https://eng.uber.com/deep-neuroevolution/)

A good survey of Deep Reinforcement Learning: [A Brief Survey of Deep Reinforcement Learning](https://arxiv.org/abs/1708.05866)

## [Table of Contents]()

* [Papers](#Papers)

* [Conferences](#Conferences)

* [Tutorials](#Tutorials)

* [Resources](#Resources)

* [Researchers](#Researchers)

## Papers

| Title | Authors | Code | Year |

| ----- | ------- | -------- | ---- |

| [Learning and Implementing Deep Learning Methods](https://www.researchgate.net/profile/Omar_Awwad3/publication/344416110_Omar_Awwad_Learning_and_Implementing_Deep_Learning_Methods/links/5f7346a6458515b7cf580db2/Omar-Awwad-Learning-and-Implementing-Deep-Learning-Methods.pdf) | Omar Awwad | not yet | 2020 |

| [Generating CNNs using Genetic Algorithm](https://nlp.fi.muni.cz/uiprojekt/ui/zachar_lev_martin2020/Documentation.pdf) | Lev Martin Zachar | not yet | 2020 |

| [A Neuroevolutionary Approach to Evolve a Flexible Neural Controller for a Morphology Changing Quadruped Robot](https://www.duo.uio.no/bitstream/handle/10852/79650/1/wonho_lee_master_thesis.pdf) | -Wonho Lee | not yet | 2020 |

| [Evolutionary Reinforcement Learning via Cooperative Coevolutionary Negatively Correlated Search](https://arxiv.org/pdf/2009.03603.pdf) | Hu Zhang, et al. | not yet | 2020 |

| [CLAN: Continuous Learning using Asynchronous Neuroevolution on Commodity Edge Devices](https://arxiv.org/pdf/2008.11881.pdf) | Parth Mannan, et al. | not yet | 2020 |

| [Using Deep Neuroevolution to train Deep Reinforcement Learning Agents](https://saliknadeem.github.io/files/drl_report.pdf) | Muhammad Salik Nadeem | not yet | 2020 |

| [Analyzing the Components of Distributed Coevolutionary GAN Training](https://arxiv.org/pdf/2008.01124.pdf) | Jamal Toutouh, et al. | [[repo](https://github.com/ALFA-group/lipizzaner-gan)] | 2020 |

| [Developmental neuronal networks as models to study the evolution of biological intelligence](https://www.irit.fr/devonn/files/2020/hintze_final.pdf) | Arend Hintze, et al. | not yet | 2020 |

| [CoNES: Convex Natural Evolutionary Strategies](https://arxiv.org/pdf/2007.08601.pdf) | Sushant Veer and Anirudha Majumdar | [[repo](https://github.com/irom-lab/conES)]| 2020 |

| [One-Shot Neural Architecture Search via Novelty Driven Sampling](https://www.ijcai.org/Proceedings/2020/0441.pdf) | Miao Zhang, et al. | [[repo](https://github.com/MiaoZhang0525/ENNAS_MASTER)] | IJCAI-20 |

| [Coevolutionary Learning of Neuromodulated Controllers for Multi-Stage and Gamified Tasks](https://www.researchgate.net/profile/Chloe_Barnes5/publication/342991590_Coevolutionary_Learning_of_Neuromodulated_Controllers_for_Multi-Stage_and_Gamified_Tasks/links/5f10907392851c1eff15d0ba/Coevolutionary-Learning-ofNeuromodulated-Controllers-for-Multi-Stage-and-Gamified-Tasks.pdf) | Chloe M. Barnes, et al. | not yet | 2020 |

| [An adaptive neuroevolution-based hyperheuristic](https://dl.acm.org/doi/abs/10.1145/3377929.3389937) | Etor Arza, et al. | [repo](https://github.com/EtorArza/GECCO2020) | GECCO ’20 |

| [Learning to walk - reward relevance within an enhanced neuroevolution approach](https://dl.acm.org/doi/abs/10.1145/3377929.3398126) | I. Colucci, et al. | not yet | GECCO ’20 |

| [Evolving neural network agents to play atari games with compact state representations](https://dl.acm.org/doi/abs/10.1145/3377929.3390072) | Adam Tupper, et al. | not yet | GECCO ’20 |

| [Online NEAT for Credit Evaluation - a Dynamic Problem with Sequential Data](https://arxiv.org/pdf/2007.02821.pdf) | Yue Liu, et al. | not yet | 2020 |

| [Exploring the evolution of GANs through quality diversity](https://dl.acm.org/doi/abs/10.1145/3377930.3389824) | Victor Costa, et al. | [[repo](https://github.com/vfcosta)]| GECCO ’20 |

| [Improving neuroevolutionary transfer learning of deep recurrent neural networks through network-aware adaptation](https://dl.acm.org/doi/abs/10.1145/3377930.3390193) | AbdElRahman ElSaid, et al.| [[repo](https://github.com/travisdesell/exact)] | GECCO ’20 |

| [Online Hyper-parameter Tuning in Off-policy Learning via Evolutionary Strategies](https://arxiv.org/pdf/2006.07554.pdf) | Yunhao Tang and Krzysztof Choromanski | not yet | 2020 |

| [NEUROEVOLUTIONARY TRANSFER LEARNING OF DEEP RECURRENT NEURAL NETWORKS THROUGH NETWORK-AWARE ADAPTATION](https://arxiv.org/pdf/2006.02655.pdf) | AbdElRahman ElSaid, et al. |  not yet |  2020 |

| [Combining a gradient-based method and an evolution strategy for multi-objective reinforcement learning](https://link.springer.com/content/pdf/10.1007/s10489-020-01702-7.pdf) | Diqi Chen, et al. | not yet | 2020 |

| [Efficient Architecture Search for Deep Neural Networks](https://www.sciencedirect.com/science/article/pii/S1877050920303859) | Ram Deepak Gottapu and Cihan H Dagli | not yet | 2020 |

| [Evolutionary Automation of Coordinated Autonomous Vehicles](http://www.nitschke-lab.uct.ac.za/sites/default/files/image_tool/images/540/Papers/2020-Evolutionary%20Automation%20of%20Coordinated%20Autonomous%20Vehicles.pdf) | Allen Huang and Geoff Nitschke | not yet | 2020 |

| [Synthetic Petri Dish: A Novel Surrogate Model for Rapid Architecture Search](https://www.researchgate.net/publication341699298_Synthetic_Petri_Dish_A_Novel_Surrogate_Model_for_Rapid_Architecture_Search) | Aditya Rawal | not yet | 2020 |

| [TRAINING ADAPTABLE NEURAL NETWORKS BASED ON EVOLVABILITY SEARCH](http://www.freepatentsonline.com/y2020/0151576.html) | Gajewski, Alexander P, et al. | not | 2020|

| [IMPROVING NEUROEVOLUTION USING ISLAND EXTINCTION AND REPOPULATION](https://arxiv.org/pdf/2005.07376.pdf) | Zimeng Lyu, et al. | [[repo](https://github.com/travisdesell/exact)] | 2020 |

| [Evolutionary Population Curriculum for Scaling Multi-Agent Reinforcement Learning](https://www.ri.cmu.edu/wp-content/uploads/2020/05/Qian_Long_thesis.pdf) | Qian LONG | not | Master Thesis 2020 |

| [Novelty Search makes Evolvability Inevitable](https://arxiv.org/pdf/2005.06224.pdf) | Stephane Doncieux, et al. | [[repo](https://github.com/robotsthatdream/diversity_algorithms)] | GECCO 2020 |

| [Genetic Deep Reinforcement Learning for Mapless Navigation](http://ifaamas.org/Proceedings/aamas2020/pdfs/p1919.pdf) | Enrico Marchesini and Alessandro Farinelli | not yet | AAMAS 2020 |

| [Accelerating Deep Neuroevolution on Distributed FPGAs for Reinforcement Learning Problems](https://arxiv.org/pdf/2005.04536.pdf) | Alexis Asseman, et al. | [[repo](https://github.com/IBM/AccDNN)] | 2020 `IBM Almaden Research Center` |

| [A Hybrid Method for Training Convolutional Neural Networks](https://arxiv.org/pdf/2005.04153.pdf)| Vasco Lopes and Paulo Fazendeiro | noy yet | 2020|

| [An Effective Maximum Entropy Exploration Approach for Deceptive Game in Reinforcement Learning](https://www.sciencedirect.com/science/article/pii/S0925231220306536) | Chunmao Lin, et al. | not yet | Neurocomputing 2020|

| [A Comparison of Evolutionary and Tree-Based Approaches for Game Feature Validation in RealTime Strategy Games with a Novel Metric ](https://www.mdpi.com/2227-7390/8/5/688) | Damijan Novak, et al. | not yet | 2020 |

| [First return then explore](https://arxiv.org/pdf/2004.12919.pdf) | Adrien Ecoffet*, Joost Huizinga∗, Joel Lehman, Kenneth O. Stanley & Jeff Clune | [[repo](https://github.com/uber-research/go-explore)] | 2020 |

|[Neuromodulated multiobjective evolutionary neurocontrollers without speciation](https://link.springer.com/article/10.1007%2Fs12065-020-00394-9) | Ian Showalter and Howard M. Schwartz  | not yet | Evolutionary Intelligence (2020) |

| [PBCS: Efficient Exploration and Exploitation Using a Synergy between Reinforcement Learning and Motion Planning](https://arxiv.org/pdf/2004.11667.pdf) | Guillaume Matheron, et al. | not yet | 2020 |

| [Efficient Evolutionary Neural Architecture Search (NAS) by Modular Inheritable Crossover](https://link.springer.com/chapter/10.1007%2F978-981-15-3425-6_61) | Hao Tan, et al. | not yet | BIC-TA 2020 |

| [Diversity Preservation in Minimal Criterion Coevolution through Resource Limitation](https://eplex.cs.ucf.edu/papers/brant_gecco20.pdf) | Jonathan C. Brant and Kenneth O. Stanley | not yet | GECCO 2020 |

| [Meta-Learning in Neural Networks: A Survey](https://arxiv.org/pdf/2004.05439.pdf) | Timothy Hospedales, et al. | not, survey | 2020 |

| [Improving Deep Reinforcement Learning with Advanced Exploration and Transfer Learning Techniques](https://dr.ntu.edu.sg/handle/10356/137772) | HAIYAN YIN | not, PhD Thesis | 2020 |

| [Using Skill Rating as Fitness on the Evolution of GANs](https://link.springer.com/chapter/10.1007%2F978-3-030-43722-0_36) | Vitor Costa | not yet | EvoApplications 2020 |

| [ModuleNet: Knowledge-inherited Neural Architecture Search](https://arxiv.org/pdf/2004.05020.pdf) | Yaran Chen, et al. | [repo](https://github.com/flymin/darts) | 2020 |

| [The Expense of Neuro-Morpho Functional Machines](http://www.nitschke-lab.uct.ac.za/sites/default/files/image_tool/images/540/Papers/2020-The%20Expense%20of%20Neuro-Morpho%20Functional%20Machines.pdf) | Scott Hallauer and Geoff Nitschke | [repo](https://github.com/robotcomplexity/2020) | 2020 |

| [Adversarial genetic programming for cyber security: a rising application domain where GP matters](https://link.springer.com/article/10.1007%2Fs10710-020-09389-y) | Una-May O’Reilly, et al. | not yet | Genetic Programming and Evolvable Machines 2020 |

| [Evolutionary recurrent neural network for image captioning](https://www.sciencedirect.com/science/article/abs/pii/S0925231220304744) | Hanzhang Wang, et al. | not yet | Neurocomputing 2020 Elsevier |

| [Learning Stabilizing Control Policies for a Tensegrity Hopper with Augmented Random Search](https://arxiv.org/pdf/2004.02641.pdf) | Vladislav Kurenkov, et al. | not yet | 2020 |

| [Evolution of Scikit-Learn Pipelines with Dynamic Structured Grammatical Evolution](https://www.researchgate.net/publication/340374650_Evolution_of_Scikit-Learn_Pipelines_with_Dynamic_Structured_Grammatical_Evolution) | Filipe Assunção, et al. | not yet | 2020 |

| [Incremental Evolution and Development of Deep Artificial Neural Networks](https://www.researchgate.net/publication340374377_Incremental_Evolution_and_Development_of_Deep_Artificial_Neural_Networks) | Filipe Assunção, et al. | [repo](https://github.com/fillassuncao/fast-denser3) | 2020 |

| [Interactive Evolution and Exploration Within Latent Level-Design Space of Generative Adversarial Networks](https://arxiv.org/pdf/2004.00151.pdf) | Jacob Schrum, et al. | [repo](https://github.com/schrum2/GameGAN) | GECCO 2020  |

| [EvoU–Net: An Evolutionary Deep Fully Convolutional NeuralNetwork for Medical Image Segmentation](https://dl.acm.org/doi/pdf/10.1145/3341105.3373856) | Tahereh Hassanzadeh, et al. | not yet | 2020 ACM |

| [Understanding Features on Evolutionar y Policy Optimizations](https://dl.acm.org/doi/pdf/10.1145/3341105.3373966) | Sangyeop Lee, et al. | not yet | 2020 ACM |

| [Fiber: A Platform for Efficient Development and Distributed Training for Reinforcement Learning and Population-Based Methods](https://arxiv.org/pdf/2003.11164.pdf) | Jiale Zhi, et al. | not yet | 2020 |

| [EVOLUTIONARY POPULATION CURRICULUM FOR SCALING MULTI-AGENT REINFORCEMENT LEARNING](https://arxiv.org/pdf/2003.10423.pdf) | Qian Long, et al. | [repo](https://github.com/qian18long/epciclr2020) | **ICLR 2020** |

| [Optimisation of Phonetic Aware Speech Recognition through Multi-objective Evolutionary Algorithms](https://www.sciencedirect.com/science/article/abs/pii/S0957417420302268) | Jordan J. Bird, et al. | not yet | [Elsevier](https://www.sciencedirect.com/journal/expert-systems-with-applications) |

| [A Brain-Inspired Framework for Evolutionary Artificial General Intelligence](https://ieeexplore.ieee.org/abstract/document/9034490) | Mohammad Nadji-Tehrani, et al. | not yet | 2020 |

|[The use of Genetic Programming for detecting the incorrect predictions of Classification Models](https://run.unl.pt/bitstream/10362/94537/1/TAA0048.pdf) | Adrianna Maria Napiórkowska | not, Master thesis | 2020 |

| [Hyper-Parameter Selection in Convolutional Neural Networks Using Microcanonical Optimization Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9037322) | AYLA GÜLCÜ and ZEKI KUŞ | not yet | 2020 |

| [Enhanced POET: Open-Ended Reinforcement Learning through Unbounded Invention of Learning Challenges and their Solutions](https://arxiv.org/pdf/2003.08536.pdf) | Rui Wang, et al. | not yet | 2020 |

| [Neuroevolution of Self-Interpretable Agents](https://arxiv.org/pdf/2003.08165.pdf) | Yujin Tang, et al. | [repo](https://attentionagent.github.io/) | 2020 |

| [META-LEARNING CURIOSITY ALGORITHMS](https://arxiv.org/pdf/2003.05325.pdf) | Ferran Alet, et al. | [repo](https://github.com/mfranzs/meta-learning-curiosity-algorithms) | **ICLR 2020** |

| [Learning feature spaces for regression with genetic programming](https://link.springer.com/article/10.1007/s10710-020-09383-4) | William La Cava and Jason H. Moore | not yet | Genetic Programming and Evolvable Machines (2020) |

| [Artificial Neural Network Trained by Plant Genetic-Inspired Optimizer](https://link.springer.com/chapter/10.1007/978-981-15-2133-1_12) | Neeraj Gupta, et al. | not yet | Frontier Applications of Nature Inspired Computation 2020 |

| [Action Unit Analysis Enhanced Facial Expression Recognition by Deep Neural Network Evolution](https://www.sciencedirect.com/science/article/abs/pii/S0925231220303891)| Ruicong Zhi, et al.| not yet | Neurocomputing 2020 Elsevier |

| [Coping with opponents: multi-objective evolutionary neural networks for fighting games](https://link.springer.com/article/10.1007/s00521-020-04794-x) | Steven Kunzel and Silja Meyer-Nieberg | not yet | Neural Computing and Applications (2020) |

| [Evolved Neuromorphic Control for High Speed Divergence-based Landings of MAVs](https://arxiv.org/pdf/2003.03118.pdf) | Jesse J. Hagenaars, et al. | [repo](https://github.com/Huizerd/evolutionary) | 2020 |

| [EGAD! an Evolved Grasping Analysis Dataset for diversity and reproducibility in robotic manipulation](https://arxiv.org/pdf/2003.01314.pdf) | Douglas Morrison, et al. | [code-blog](https://dougsm.github.io/egad/) | 2020 |

| [Scaling MAP-Elites to Deep Neuroevolution](https://www.researchgate.net/publication/339710807_Scaling_MAP-Elites_to_Deep_Neuroevolution) | Cedric Colac, et al. | not yet | 2020 |

| [Backpropamine: training self-modifying neural networks with differentiable neuromodulated plasticity](https://www.researchgate.net/publication/339498081_Backpropamine_training_self-modifying_neural_networks_with_differentiable_neuromodulated_plasticity) | Aditya Rawal, Jeff Clune, Kenneth O. Stanley | not yet | 2020 |

| [AN EVOLUTIONARY DEEP LEARNING METHOD FOR SHORT-TERM WIND SPEED PREDICTION: A CASE STUDY OF THE LILLGRUND OFFSHORE WIND FARM](https://arxiv.org/pdf/2002.09106.pdf) | Mehdi Neshat, et al. | not yet | 2020|

| [Accelerating Reinforcement Learning with a Directional-Gaussian-Smoothing Evolution Strategy](https://arxiv.org/pdf/2002.09077.pdf) | Jiaxin Zhang, et al. | not yet | 2020 |

| [Comparison Between Stochastic Gradient Descent and VLE Metaheuristic for Optimizing Matrix Factorization](https://link.springer.com/chapter/10.1007/978-3-030-41913-4_13) | Juan A. Gómez-Pulido, et al. | not yet | OLA 2020 |

| [Effective Reinforcement Learning through Evolutionary Surrogate-Assisted Prescription](https://arxiv.org/pdf/2002.05368.pdf) | Olivier Francon, et al. | not yet | 2020 |

| [Highly Efficient Deep Intelligence via Multi-Parent Evolutionary Synthesis of Deep Neural Networks](https://uwspace.uwaterloo.ca/bitstream/handle/10012/15627/Chung_Audrey.pdf?sequence=1&isAllowed=y) | Audrey Chung | Master Thesis | 2020 |

| [NEUROEVOLUTION OF NEURAL NETWORK ARCHITECTURES USING CODEEPNEAT AND KERAS](https://arxiv.org/pdf/2002.04634.pdf) | Jonas da Silveira Bohrer, et al. | [repo](https://github.com/sbcblab/Keras-CoDeepNEAT) | 2020 |

| [Horizontal gene transfer for recombining graphs](http://eprints.whiterose.ac.uk/155474/8/Atkinson2020_Article_HorizontalGeneTransferForRecom.pdf) | Timothy Atkinson, et al. | [repo](https://github.com/UoYCS-plasma/EGGP) | Genetic Programming and Evolvable Machines (2020) |

| [Evolutionary music: applying evolutionary computation to the art of creating music](https://link.springer.com/article/10.1007/s10710-020-09380-7) | Roisin Loughran, et al. | not yet | Genetic Programming and Evolvable Machines (2020) |

| [Improving the Performance of Evolutionary Algorithms via Gradient-Based Initialization](http://cs230.stanford.edu/projects_fall_2019/reports/26262118.pdf) | Chris Waites, et al | not yet | 2020 |

| [Evolving Loss Functions With Multivariate Taylor Polynomial Parameterizations](https://arxiv.org/pdf/2002.00059.pdf) | Santiago Gonzalez and  Risto Miikkulainen | [repo](https://github.com/sgonzalez/SwiftCMA) | 2020 |

| [Evolving Neural Networks through a Reverse Encoding Tree](https://arxiv.org/pdf/2002.00539.pdf) | Haoling Zhang, et al. | [repo](https://github.com/HaolingZHANG/ReverseEncodingTree)| 2020 |

| [Evolutionary LSTM-FCN networks for pattern classification in industrial processes](https://www.sciencedirect.com/science/article/abs/pii/S2210650219301270) | Patxi Ortego, et al. | not yet | Swarm and Evolutionary Computation, May 2020 |

| [Evolving deep neural networks using coevolutionary algorithms with multi-population strategy](https://link.springer.com/article/10.1007/s00521-020-04749-2) | Sreenivas Sremath Tirumala | not yet | Neural Computing and Applications 2020 |

| [Hierarchy and co-evolution processes in urban systems](https://arxiv.org/pdf/2001.11989.pdf) | Juste Raimbault | [repo](https://github.com/JusteRaimbault/CoevolutionNwTerritories) | 2020 |

| [A Study of Fitness Landscapes for Neuroevolution](https://arxiv.org/pdf/2001.11272.pdf) | Nuno M. Rodrigues, et al. | not yet | 2020 |

| [Combining Evolution and Learning in Computational Ecosystems](https://www.degruyter.com/downloadpdf/j/jagi.2020.11.issue-1/jagi-2020-0001/jagi-2020-0001.pdf) | Claes Strannegård, et al. | not yet | 2020 |

| [Examining Hyperparameters of Neural Networks Trained Using Local Search](https://www.researchgate.net/profile/Ahmed_Aly13/publication/338501734_Examining_Hyperparameters_of_Neural_Networks_Trained_Using_Local_Search/links/5e1820fc299bf10bc3a09fe5/Examining-Hyperparameters-of-Neural-Networks-Trained-Using-Local-Search.pdf) | Ahmed Aly, et al. | not yet | 2020 |

| [Analysing Deep Reinforcement Learning Agents Trained with Domain Randomisation](https://arxiv.org/pdf/1912.08324.pdf) | Tianhong Dai, et al. | not yet | 2020 |

| [POPULATION-GUIDED PARALLEL POLICY SEARCH FOR REINFORCEMENT LEARNING](https://arxiv.org/pdf/2001.02907.pdf) | Whiyoung Jung, et al. | [repo](https://github.com/wyjung0625/p3s) | ICLR 2020 |

| [IMPROVING DEEP NEUROEVOLUTION VIA DEEP INNOVATION PROTECTION](https://arxiv.org/pdf/2001.01683.pdf) | Sebastian Risi and Kenneth O. Stanley | [repo](https://github.com/uber-research/ga-world-models/) | 2020 |

| [Evolutionary NetArchitecture Search for Deep Neural Networks Pruning](https://dl.acm.org/doi/abs/10.1145/3377713.3377739) | Shuxin Chen, et al. | not yet | 2019 |

|[AI-GAs: AI-generating algorithms, an alternate paradigm for producing general artificial intelligence](https://arxiv.org/abs/1905.10985) | Jeff Clune | not yet | 2019 |

| [Differential Evolution for Neural Networks Optimization](https://www.researchgate.net/publication/338404662_Differential_Evolution_for_Neural_Networks_Optimization) | Marco Baioletti, et al. | not yet | Mathematics 2020 |

| [Neuro-Evolution Search Methodologies for Collective Self-Driving Vehicles](https://open.uct.ac.za/bitstream/handle/11427/31252/thesis_sci_2019_huang_chien_lun_allen.pdf?sequence=1) | Chien-Lun (Allen) Huang | Master thesis | 2019 |

| [Using Neuroevolved Binary Neural Networks to solve reinforcement learning environments](https://ieeexplore.ieee.org/abstract/document/8953134) | Raul Valencia, et al. | not yet | 2019 IEEE APCCAS|

| [Neuroevolution with CMA-ES for Real-time Gain Tuning of a Car-like Robot Controller](https://pdfs.semanticscholar.org/c90b/e74c07dde44a77f1b04b0656cfbc1ffc6391.pdf) | Ashley Hill, et al | not yet | ICINCO 2019 |

| [Learning to grow: control of materials self-assembly using evolutionary reinforcement learning](https://arxiv.org/pdf/1912.08333.pdf) | Stephen Whitelam, et al. | not yet | 2019 |

| [Network of Evolvable Neural Units: Evolving to Learn at a Synaptic Level](https://arxiv.org/pdf/1912.07589.pdf) | Paul Bertens, et al. | not yet | 2019 |

| [GENERATIVE TEACHING NETWORKS: ACCELERATING NEURAL ARCHITECTURE SEARCH BY LEARNING TO GENERATE SYNTHETIC TRAINING DATA](https://arxiv.org/pdf/1912.07768.pdf) | Felipe Petroski Such, et al. | not yet | 2019 |

| [Efficacy of Modern Neuro-Evolutionary Strategies for Continuous Control Optimization](https://arxiv.org/pdf/1912.05239.pdf) | Paolo Pagliuca, et al. | not yet | 2019 |

| [GAIM: A C++ library for Genetic Algorithms and Island Models](https://www.theoj.org/joss-papers/joss.01839/10.21105.joss.01839.pdf) | Georgios Detorakis, et al. | [[repo](https://gitlab.com/gdetor/genetic_alg)] | JOSS 2019 |

| [Dynamic Facial Feature Learning by Deep Evolutionary Neural Networks](https://link.springer.com/chapter/10.1007/978-981-15-1925-3_23) | Ruicong Zhi, et al. | not yet | CyberDI 2019 |

| [Automatic Design of Convolutional Neural Networks using Grammatical Evolution](https://ieeexplore.ieee.org/abstract/document/8923816) | Ricardo Henrique Remes de Lima, et al. | not yet | BRACIS 2019 |

| [Q-NAS Revisited: Exploring Evolution Fitness to Improve Efficiency](https://ieeexplore.ieee.org/abstract/document/8923858) | Daniela Szwarcman, et al | noy yet | BRACIS 2019 |

| [An Evolutionary Approach to Compact DAG Neural Network Optimization](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8907813) | Carter Chiu, et al. | not yet | 2019 |

| [Multi-Criterion Evolutionary Design of Deep Convolutional Neural Networks](https://arxiv.org/pdf/1912.01369.pdf) | Zhichao Lu, et al. | [[repo](https://github.com/ianwhale/nsga-net)] | 2019 |

|[A Survey on the Latest Development of Machine Learning in Genetic Algorithm and Particle Swarm Optimization](https://rd.springer.com/chapter/10.1007/978-981-15-0994-0_6) | Dipti Kapoor Sarmah | not yet | [Optimization in Machine Learning and Applications](https://rd.springer.com/book/10.1007/978-981-15-0994-0) |

| [A Graph-Based Encoding for Evolutionary Convolutional Neural Network Architecture Design](https://ieeexplore.ieee.org/document/8790093) | William Irwin-Harris, et al. | not yet | 2019 IEEE Congress on Evolutionary Computation (CEC) |

| [Auto-creation of Effective Neural Network Architecture by Evolutionary Algorithm and ResNet for Image Classification](https://ieeexplore.ieee.org/abstract/document/8914267) | Zefeng Chen, et al. | not yet |  2019 IEEE International Conference on Systems, Man and Cybernetics (SMC) |

| [Evolving Knowledge And Structure Through Evolution-based Neural Architecture Search](https://ntnuopen.ntnu.no/ntnu-xmlui/handle/11250/2630150) | Magnus Poppe Wang | not | Master Thesis 2019 |

| [Procedural Generation of Quests for Games Using Genetic Algorithms and Automated Planning ](https://www.researchgate.net/profile/Edirlei_Soares_De_Lima/publication/337103452_Procedural_Generation_of_Quests_for_Games_Using_Genetic_Algorithms_and_Automated_Planning/links/5dc53de74585151435f3bcb8/Procedural-Generation-of-Quests-for-Games-Using-Genetic-Algorithms-and-Automated-Planning.pdf) | Edirlei Soares de Lima, et al. | not yet | ```SBGames 2019``` |

| [Culturally Evolved GANs for generating Fake Stroke Faces](https://www.researchgate.net/profile/Kaitav_Mehta/publication/334811480_Culturally_Evolved_GANs_for_Generating_Fake_Stroke_Faces/links/5db4dbbf299bf111d4d04521/Culturally-Evolved-GANs-for-Generating-Fake-Stroke-Faces.pdf) | Kaitav Mehta, et al. | code not yet | 2019 |

| [Neuroevolutionary Training of Deep Convolutional Generative Adversarial Networks](https://www.researchgate.net/publication/336832734_Neuroevolutionary_Training_of_Deep_Convolutional_GANspdf) | Kaitav Mehta | not | Master Thesis 2019 |

| [UMA ESTRUTURA PARA EXECUCAO DE REDES NEURAIS EVOLUTIVAS NA GPU](https://www.cos.ufrj.br/uploadfile/publicacao/2932.pdf) | Jorge Rama Krsna Mandoju | not | Master Thesis 2019 |

| [An Overview of Open-Ended Evolution: Editorial Introduction to the Open-Ended Evolution II Special Issue](https://arxiv.org/abs/1909.04430) | Norman Packard, et al. | not yet | 2019 |

| [Deep neural network architecture search using network morphism](https://ieeexplore.ieee.org/abstract/document/8864624) | Arkadiusz Kwasigroch, et al. | [[repo](https://github.com/akwasigroch/NAS_network_morphism)]![github](github.jpg) | 2019 |

| [Neuroevolutive Algorithms for Learning Gaits in Legged Robots](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8852635) | Pablo Reyes, et al. | not yet | 2019 |

| [Neural Architecture Evolution in Deep Reinforcement Learning for Continuous Control](https://arxiv.org/pdf/1910.12824.pdf) | Jörg K.H. Franke and Gregor Koehler, et al. | not yet | 2019 |

| [Efficient Decoupled Neural Architecture Search by Structure and Operation Sampling](https://arxiv.org/pdf/1910.10397.pdf) | Heung-Chang Lee, et al. | [repo](https://github.com/logue311/EDNAS) | 2019 |

| [ES-MAML: Simple Hessian-Free Meta Learning](https://arxiv.org/pdf/1910.01215.pdf) | Xingyou Song, et al. | not yet | 2019 |

| [Empirical study on the performance of Neuro Evolution of Augmenting Topologies (NEAT)](http://ailab.ijs.si/dunja/SiKDD2019/Papers/Vicic_Final.pdf) | Domen Vake, et al. | [repo](https://github.com/VakeDomen/NEAT-driving) | 2019 |

| [Improving Gradient Estimation in Evolutionary Strategies With Past Descent Directions](https://arxiv.org/pdf/1910.05268.pdf) | Florian Meier and Asier Mujika | not yet | 2019 |

| [Algebraic Neural Architecture Representation, Evolutionary Neural Architecture Search, and Novelty Search in Deep Reinforcement Learning](https://ir.lib.uwo.ca/cgi/viewcontent.cgi?article=8458&context=etd) | Ethan C. Jackson | not  | PhD Thesis 2019 |

| [GACNN: TRAINING DEEP CONVOLUTIONAL NEURAL NETWORKS WITH GENETIC ALGORITHM](https://arxiv.org/pdf/1909.13354.pdf) | Parsa Esfahanian and Mohammad Akhavan | not yet  | 2019 |

| [Implicit Multi-Objective Coevolutionary Algorithms](https://atrium2.lib.uoguelph.ca/xmlui/bitstream/handle/10214/17493/Akinola_Adefunke_201910_Msc.pdf?sequence=5&isAllowed=y) | Adefunke Akinola | not | Master Thesis 2019 |

| [CEM-RL: Combining evolutionary and gradient-based methods for policy search](https://arxiv.org/abs/1810.01222) | Aloïs Pourchot, Olivier Sigaud | [repo](https://github.com/apourchot/CEM-RL)![github](github.jpg) | ICLR 2019 |

| [Efficient Multi-objective Neural Architecture Search via Lamarckian Evolution](https://arxiv.org/abs/1804.09081) | Thomas Elsken, Jan Hendrik Metzen, Frank Hutter | [openreview](https://openreview.net/forum?id=ByME42AqK7) | 2018, ICLR2019 |

| [Exploring Randomly Wired Neural Networks for Image Recognition](https://arxiv.org/abs/1904.01569) | Saining Xie, Alexander Kirillov, Ross Girshick, Kaiming He  | not yet | 2019 |

| [Designing neural networks through neuroevolution](https://www.nature.com/articles/s42256-018-0006-z.pdf?origin=ppub) | Kenneth O. Stanley, Jeff Clune, Joel Lehman and Risto Miikkulainen | it is a letter | Nature machine intelligence January 2019|

| [Guided evolutionary strategies: escaping the curse of dimensionality in random search](https://arxiv.org/abs/1806.10230) | Niru Maheswaranathan, Luke Metz, George Tucker, Dami Choi, Jascha Sohl-Dickstein| [repo](https://github.com/brain-research/guided-evolutionary-strategies) ![github](github.jpg) | ICML 2019|

| [Collaborative Evolutionary Reinforcement Learning](https://arxiv.org/abs/1905.00976v2) | Shauharda Khadka, Somdeb Majumdar, Tarek Nassar, Zach Dwiel, Evren Tumer, Santiago Miret, Yinyin Liu, Kagan Tumer | [blog](https://deeplearn.org/arxiv/73440/collaborative-evolutionary-reinforcement-learning) | ICML 2019 |

| [Trust Region Evolution Strategies](https://pdfs.semanticscholar.org/6557/f9e7832dd127ab3ea2bcd0d1a6b924d4efc2.pdf) | Guoqing Liu et al. | not yet | AAAI 2019 |

| [Deep Neuroevolution of Recurrent and Discrete World Models](https://arxiv.org/abs/1906.08857) | Sebastian Risi, Kenneth O. Stanley | [repo](https://github.com/sebastianrisi/ga-world-models) | 2019 |

| [Proximal Distilled Evolutionary Reinforcement Learning](https://arxiv.org/abs/1906.09807) | Cristian Bodnar, Ben Day, Pietro Lio' | not yet | AAAI 2019 |

| [POET: open-ended coevolution of environments and their optimized solutions](https://www.researchgate.net/publication/334220089_POET_open-ended_coevolution_of_environments_and_their_optimized_solutions) | Rui Wang, Joel Lehman, Jeff Clune and Kenneth O. Stanley | not yet | GECCO 2019 |

| [COEGAN: evaluating the coevolution effect in generative adversarial networks](https://www.researchgate.net/publication/337944622_COEGAN_Evaluating_the_Coevolution_Effect_in_Generative_Adversarial_Networks) | V. Costa, N. Lourenço, J. Correia, and P. Machado |[repo](https://github.com/vfcosta/coegan) | GECCO 2019 |

| [Evolution and self-teaching in neural networks: another comparison when the agent is more primitively conscious]() | Nam Le | not yet  | GECCO 2019 |

| [Diverse Agents for Ad-Hoc Cooperation in Hanabi](https://arxiv.org/abs/1907.03840) | Rodrigo Canaan, Julian Togelius, Andy Nealen, Stefan Menzel | not yet | CoG 2019 |

| [EPNAS: Efficient Progressive Neural Architecture Search](https://arxiv.org/abs/1907.04648) | Yanqi Zhou, Peng Wang, Sercan Arik, Haonan Yu, Syed Zawad, Feng Yan, Greg Diamos | not yet | 2019 |

| [Acoustic Model Optimization Based On Evolutionary Stochastic Gradient Descent with Anchors for Automatic Speech Recognition](https://arxiv.org/abs/1907.04882)  | Xiaodong Cui, Michael Picheny (IBM Research)| not yet | Interspeech 2019 |

| [Fast DENSER: Efficient Deep NeuroEvolution](https://www.researchgate.net/publication/332306893_Fast_DENSER_Efficient_Deep_NeuroEvolution) | Filipe Assunção, Nuno Lourenço, Penousal Machado, Bernardete Ribeiro | [repo](https://github.com/fillassuncao/f-denser) | EuroGP 2019 |

| [AlphaStar: An Evolutionary Computation Perspective](https://arxiv.org/abs/1902.01724) | Kai Arulkumaran, Antoine Cully, Julian Togelius | not yet | GECCO 2019 |

| [Automatic Design of Artificial Neural Networks for Gamma-Ray Detection](https://www.researchgate.net/publication/332977799_Automatic_Design_of_Artificial_Neural_Networks_for_Gamma-Ray_Detection) | Filipe Assunção, João Correia, Rúben Conceição, Mário Pimenta, Bernardo Tomé, Nuno Lourenço, Penousal Machado | not yet | 2019 |

| [Evolvability ES: Scalable and Direct Optimization of Evolvability](https://arxiv.org/abs/1907.06077) | Alexander Gajewski, Jeff Clune, Kenneth O. Stanley, Joel Lehman | [repo](https://github.com/uber-research/Evolvability-ES) | GECCO 2019 |

| [Towards continual reinforcement learning through evolutionary meta-learning](https://dl.acm.org/citation.cfm?id=3322044) | Djordje Grbic and Sebastian Risi  | not yet  | GECCO 2019|

| [Automated Neural Network Construction with Similarity Sensitive Evolutionary Algorithms](http://rvc.eng.miami.edu/Paper/2019/IRI19_EA.pdf) | Haiman Tian et al. | not yet| 2019 |

| [Provably Robust Blackbox Optimization for Reinforcement Learning](https://arxiv.org/abs/1903.02993) | Krzysztof Choromanski, Aldo Pacchiano et al. | not yet  | 2019 |

| [Go-Explore: a New Approach for Hard-Exploration Problems](https://arxiv.org/abs/1901.10995) | Adrien Ecoffet, Joost Huizinga, Joel Lehman, Kenneth O. Stanley, Jeff Clune | not yet | 2019 |

| [Culturally Evolved GANs for Generating Fake Stroke Faces](https://www.researchgate.net/publication/334811480_Culturally_Evolved_GANs_for_Generating_Fake_Stroke_Faces) | Kaitav Mehta et al. | not yet  | ICTS4eHealth'19 |

| [An Evolution Strategy with Progressive Episode Lengths for Playing Games](https://ml.informatik.uni-freiburg.de/papers/19-IJCAI_PEL.pdf) | Lior Fuks, Noor Awad , Frank Hutter and Marius Lindauer | [repo](https://github.com/liorfuks/meta_pop) | IJCAI 2019 |

| [On Hard Exploration for Reinforcement Learning: A Case Study in Pommerman](https://arxiv.org/abs/1907.11788) | Chao Gao, Bilal Kartal, Pablo Hernandez-Leal, Matthew E. Taylor | not yet | 2019 |

| [A Knee-Guided Evolutionary Algorithm for Compressing Deep Neural Networks](https://www.researchgate.net/publication/334782953_A_Knee-Guided_Evolutionary_Algorithm_for_Compressing_Deep_Neural_Networks) | Yao Zhou, et al. | not yet | 2019 |

| [Multi-task Deep Reinforcement Learning with Evolutionary Algorithm and Policy Gradients Method in 3D Control Tasks](https://www.researchgate.net/publication/334809110_Multi-task_Deep_Reinforcement_Learning_with_Evolutionary_Algorithm_and_Policy_Gradients_Method_in_3D_Control_Tasks) | Shota Imai et al. | not yet | In book: Big Data, Cloud Computing, and Data Science Engineering 2019 |

| [Evolutionary deep learning](https://open.uct.ac.za/bitstream/handle/11427/30357/thesis_sci_2019_dufourq_emmanuel.pdf?sequence=1) | E Dufourq | not | PhD thesis 2019 |

| [Guiding Evolutionary Strategies with Off-Policy Actor-Critic](https://robintyh1.github.io/papers/Tang2019CEMACER.pdf) | Yunhao Tang | not yet | 2019 |

| [Construction of Macro Actions for Deep Reinforcement Learning](https://arxiv.org/abs/1908.01478) | Yi-Hsiang Chang, Kuan-Yu Chang, Henry Kuo, Chun-Yi Lee | not yet | 2019 |

| [Fast Automatic Optimisation of CNN Architectures for Image Classification Using Genetic Algorithm](https://ieeexplore.ieee.org/abstract/document/8790197) | Ali Bakhshi, et al. | not yet | CEC 2019 |

| [Memetic Evolution Strategy for Reinforcement Learning](https://ieeexplore.ieee.org/abstract/document/8789935) | Xinghua Qu, et al. | not yet | 2019 |

| [Epigenetic evolution of deep convolutional models](http://www.cse.unsw.edu.au/~blair/pubs/2019HadjiivanovBlairCEC.pdf) | Alexander Hadjiivanov and Alan Blair | not yet | CEC 2019 |

| [A CROSS-DATA SET EVALUATION OF GENETICALLY EVOLVED NEURAL NETWORK ARCHITECTURES](http://jbox.gmu.edu/bitstream/handle/1920/11479/Gelman_thesis_2019.pdf?sequence=1&isAllowed=y) | Ben Gelman | not yet | Master Thesis 2019 |

| [Architecture Search by Estimation of Network Structure Distributions](https://arxiv.org/pdf/1908.06886.pdf) | Anton Muravev, et al. | not yet | 2019 |

| [Evolving unsupervised neural networks for Slither.io](https://dl.acm.org/citation.cfm?id=3341837) | 	Mitchell Miller, et al. | [Slither.io](http://slither.io/) | FDG 2019 |

| [Scalable Reinforcement-Learning-Based Neural Architecture Search for Cancer Deep Learning Research](https://arxiv.org/pdf/1909.00311.pdf) | Prasanna Balaprakash and Romain Egele, et al. | not yet | 2019 |

| [Using Neuroevolution for Predicting Mobile Marketing Conversion](https://rd.springer.com/chapter/10.1007/978-3-030-30244-3_31) | Pedro José Pereira, et al. | not yet | LNCS, volume 11805 |

|[A Restart-based Rank-1 Evolution Strategy for Reinforcement Learning](https://www.ijcai.org/proceedings/2019/0295.pdf) | Zefeng Chen, et al. | not yet  | IJCAI-19 |

| [Evolution of Kiting Behavior in a Two Player Combat Problem](http://ieee-cog.org/papers/paper_116.pdf) | Pavlos Androulakakis and Zachariah E. Fuchs | not yet | IEEE COG 2019 |

| [Learning to Select Mates in Evolving Non-playable Characters](http://ieee-cog.org/papers/paper_66.pdf) | Dylan R. Ashley, et al. | not yet  | IEEE COG 2019 |

| [MULTI-SPECIES EVOLUTIONARY ALGORITHMS FOR COMPLEX OPTIMISATION PROBLEMS](https://etheses.bham.ac.uk/id/eprint/8893/1/Lu2018PhD.pdf) | XIAOFEN LU | not yet | PhD thesis at University of Birmingham |

| [ATTRACTION-REPULSION ACTOR-CRITIC FOR CONTINUOUS CONTROL REINFORCEMENT LEARNING](https://arxiv.org/pdf/1909.07543.pdf) | Thang Doan and Bogdan Mazoure, et al. | not yet | 2019 |

| [Comparative Study of Neuro-Evolution Algorithms in Reinforcement Learning for Self-Driving Cars](https://www.dpublication.com/wp-content/uploads/2019/07/6-F186.pdf) |  Ahmed AbuZekry, et al. | not yet | 2019 |

| [An Empirical Exploration of Deep Recurrent Connections and Memory Cells Using Neuro-Evolution](https://arxiv.org/pdf/1909.09502.pdf) | Travis J. Desell, et al.  | [repo](https://github.com/travisdesell/exact) | 2019 |

| [THE ANT SWARM NEURO-EVOLUTION PROCEDURE FOR OPTIMIZING RECURRENT NETWORKS](https://arxiv.org/pdf/1909.11849.pdf) | AbdElRahman A. ElSaid, et al. | not yet | 2019 |

| [Correlation Analysis-Based Neural Network Self-Organizing Genetic Evolutionary Algorithm](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8843933) | ZENGHAO CHAI, et al. | not yet | IEEE Access 2019 |

| [Learning Task-specific Activation Functions using Genetic Programming](https://pdfs.semanticscholar.org/4be0/701f2d2a34ffb746595bcb251c091ff4a702.pdf) | Mina Basirat and Peter M. Roth | [repo](https://github.com/jliphard/DeepEvolve) | 2019 |

| [A HYBRID NEURAL NETWORK AND GENETIC PROGRAMMING APPROACH TO THE AUTOMATIC CONSTRUCTION OF COMPUTER VISION SYSTEMS](http://repository.essex.ac.uk/25359/1/Cameron_MRES_Thesis.pdf) | Cameron P. Kyle-Davidson | not | Master Thesis 2019 |

| [Novelty Search for Deep Reinforcement Learning Policy Network Weights by Action Sequence Edit Metric Distance](https://arxiv.org/pdf/1902.03142.pdf) | Ethan C. Jackson and Mark Daley | [repo](https://github.com/ethancjackson/NoveltySearchLevenshtein) | Submitted to GECCO 2019 |

| [Playing Atari with Six Neurons](https://arxiv.org/abs/1806.01363) | Giuseppe Cuccu, Julian Togelius, Philippe Cudre-Mauroux | [[repo](https://github.com/giuse/DNE/tree/nips2018)] ![github](github.jpg) | 2018, AAMAS 2019 |

| [Simple random search provides a competitive approach to reinforcement learning](https://arxiv.org/abs/1803.07055)| Horia Mania, Aurelia Guy, Benjamin Recht | [[repo](https://github.com/modestyachts/ARS)]![github](github.jpg) | 2018 |

| [Regularized Evolution for Image Classifier Architecture Search](https://arxiv.org/abs/1802.01548) | Esteban Real, Alok Aggarwal, Yanping Huang, Quoc V Le | [repocolab](https://colab.research.google.com/github/google-research/google-research/blob/master/evolution/regularized_evolution_algorithm/regularized_evolution.ipynb) | 2018, AAAI 2019 |

| [Evolution-Guided Policy Gradient in Reinforcement Learning](https://arxiv.org/abs/1805.07917) | Shauharda Khadka, Kagan Tumer | [repo](https://github.com/ShawK91/erl_paper_nips18) ![github](github.jpg)| NIPS 2018 |

| [Evolutionary Stochastic Gradient Descent for Optimization of Deep Neural Networks](https://arxiv.org/abs/1810.06773)| Xiaodong Cui, Wei Zhang, Zoltán Tüske, Michael Picheny | not yet | NIPS 2018 |

| [Experimental Evaluation of Metaheuristic Optimization of Gradients as an Alternative to Backpropagation](https://ieeexplore.ieee.org/abstract/document/8780709) | Oleksandr Zavalnyi et al. |  not yet  | 2018 |

| [Evolution Strategies as a Scalable Alternative to Reinforcement Learning](https://arxiv.org/abs/1703.03864) | Tim Salimans, Jonathan Ho, Xi Chen, Szymon Sidor, Ilya Sutskever | [[repo](https://github.com/openai/evolution-strategies-starter)]![github](github.jpg) [[blog](https://blog.openai.com/evolution-strategies/)] | 2017 |

| [Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning](https://arxiv.org/abs/1712.06567) | Felipe Petroski Such, Vashisht Madhavan, Edoardo Conti, Joel Lehman, Kenneth O. Stanley, Jeff Clune | [[repo](https://github.com/uber-research/deep-neuroevolution)]![github](github.jpg) [[blog](https://eng.uber.com/deep-neuroevolution/)]| 2017 |

| [Safe Mutations for Deep and Recurrent Neural Networks through Output Gradients](https://arxiv.org/abs/1712.06563) | Joel Lehman, Jay Chen, Jeff Clune, Kenneth O. Stanley | [repo](https://github.com/uber-research/safemutations)![github](github.jpg) | 2017 |

| [Improving Exploration in Evolution Strategies for Deep Reinforcement Learning via a Population of Novelty-Seeking Agents](https://arxiv.org/abs/1712.06560)| Edoardo Conti, Vashisht Madhavan, Felipe Petroski Such, Joel Lehman, Kenneth O. Stanley, Jeff Clune | [repo](https://github.com/uber-research/deep-neuroevolution)![github](github.jpg) | 2017, NIPS 2018 |

| [On the Relationship Between the OpenAI Evolution Strategy and Stochastic Gradient Descent](https://arxiv.org/abs/1712.06564) | Xingwen Zhang, Jeff Clune, Kenneth O. Stanley | [blog](https://eng.uber.com/deep-neuroevolution/) | 2017 |

| [ES Is More Than Just a Traditional Finite-Difference Approximator](https://arxiv.org/abs/1712.06568) | Joel Lehman, Jay Chen, Jeff Clune, Kenneth O. Stanley | [blog](https://eng.uber.com/deep-neuroevolution/) | 2017 |

## Conferences

- Genetic and evolutionary computation

  * [GECCO](https://gecco-2019.sigevo.org/index.html/HomePage)

  * [CEC](http://cec2019.org/)

- Machine learning

  * [ICLR](https://iclr.cc/)  

- Artificial intelligence  

  * [AAAI](https://www.aaai.org/)

  * [IJCAI](https://www.ijcai.org/)

- Games

  * [IEEE-COG](http://ieee-cog.org/)

## Tutorials

* [Paper experiments replication: Deep Neuroevolution Uber](https://towardsdatascience.com/paper-repro-deep-neuroevolution-756871e00a66)

* [Genetic Algorithm for 2D function-Matlab](https://github.com/Alro10/genetic-algorithm-optimization)[github](github.jpg)

## Resources

* [VINE: An Open Source Interactive Data Visualization Tool for Neuroevolution](https://eng.uber.com/vine/) by Uber AI Labs.

* [EvoGrad: A Lightweight Library for Gradient-Based Evolution](https://eng.uber.com/evograd/) by Uber AI Labs.

* [POET](https://eng.uber.com/poet-open-ended-deep-learning/) by Uber AI Labs.

* [Nevergrad - A gradient-free optimization platform](https://github.com/facebookresearch/nevergrad) by Facebook Research.

## Researchers

* [Kenneth O. Stanley](https://eng.uber.com/author/kenneth-stanley/)- Uber AI Labs.

* [Jeff Clune](https://eng.uber.com/author/jeff-clune/)- Open AI.

* [Risto Miikkulainen](https://scholar.google.com/citations?user=2SmbjHAAAAAJ&hl=es)- University of Texas at Austin.

* [Nuno Lourenço](https://www.cisuc.uc.pt/people/show/2906)- University of Coimbra.

*Know of a recent paper? Send a pull request or open an issue!* :+1: