| Abandoning objectives: Evolution through the search for novelty alone J Lehman, KO Stanley Evolutionary computation 19 (2), 189-223, 2011 | 693 | 2011 |
| Exploiting open-endedness to solve problems through the search for novelty. J Lehman, KO Stanley ALIFE, 329-336, 2008 | 471 | 2008 |
| Deep neuroevolution: Genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning FP Such, V Madhavan, E Conti, J Lehman, KO Stanley, J Clune arXiv preprint arXiv:1712.06567, 2017 | 398 | 2017 |
| An intriguing failing of convolutional neural networks and the coordconv solution R Liu, J Lehman, P Molino, FP Such, E Frank, A Sergeev, J Yosinski Advances in neural information processing systems, 9605-9616, 2018 | 264 | 2018 |
| Evolving a diversity of virtual creatures through novelty search and local competition J Lehman, KO Stanley Proceedings of the 13th annual conference on Genetic and evolutionary …, 2011 | 263 | 2011 |
| Designing neural networks through neuroevolution KO Stanley, J Clune, J Lehman, R Miikkulainen Nature Machine Intelligence 1 (1), 24-35, 2019 | 181 | 2019 |
| A neuroevolution approach to general atari game playing M Hausknecht, J Lehman, R Miikkulainen, P Stone IEEE Transactions on Computational Intelligence and AI in Games 6 (4), 355-366, 2014 | 175 | 2014 |
| Improving exploration in evolution strategies for deep reinforcement learning via a population of novelty-seeking agents E Conti, V Madhavan, FP Such, J Lehman, K Stanley, J Clune Advances in neural information processing systems, 5027-5038, 2018 | 165 | 2018 |
| The surprising creativity of digital evolution: A collection of anecdotes from the evolutionary computation and artificial life research communities J Lehman, J Clune, D Misevic, C Adami, L Altenberg, J Beaulieu, ... Artificial Life 26 (2), 274-306, 2020 | 141 | 2020 |
| Go-explore: a new approach for hard-exploration problems A Ecoffet, J Huizinga, J Lehman, KO Stanley, J Clune arXiv preprint arXiv:1901.10995, 2019 | 140 | 2019 |
| Revising the evolutionary computation abstraction: minimal criteria novelty search J Lehman, KO Stanley Proceedings of the 12th annual conference on Genetic and evolutionary …, 2010 | 117 | 2010 |
| Efficiently evolving programs through the search for novelty J Lehman, KO Stanley Proceedings of the 12th annual conference on Genetic and evolutionary …, 2010 | 105 | 2010 |
| Why greatness cannot be planned: The myth of the objective KO Stanley, J Lehman Springer, 2015 | 93 | 2015 |
| Novelty search and the problem with objectives J Lehman, KO Stanley Genetic programming theory and practice IX, 37-56, 2011 | 93 | 2011 |
| Combining search-based procedural content generation and social gaming in the petalz video game S Risi, J Lehman, D D'Ambrosio, R Hall, K Stanley Proceedings of the AAAI Conference on Artificial Intelligence and …, 2012 | 73 | 2012 |
| Evolving policy geometry for scalable multiagent learning DB D'Ambrosio, J Lehman, S Risi, KO Stanley Proceedings of the 9th International Conference on Autonomous Agents and …, 2010 | 72 | 2010 |
| Effective diversity maintenance in deceptive domains J Lehman, KO Stanley, R Miikkulainen Proceedings of the 15th annual conference on Genetic and evolutionary …, 2013 | 67 | 2013 |
| Paired open-ended trailblazer (poet): Endlessly generating increasingly complex and diverse learning environments and their solutions R Wang, J Lehman, J Clune, KO Stanley arXiv preprint arXiv:1901.01753, 2019 | 63 | 2019 |
| Evolvability is inevitable: Increasing evolvability without the pressure to adapt J Lehman, KO Stanley PloS one 8 (4), e62186, 2013 | 58 | 2013 |
| ES is more than just a traditional finite-difference approximator J Lehman, J Chen, J Clune, KO Stanley Proceedings of the Genetic and Evolutionary Computation Conference, 450-457, 2018 | 56 | 2018 |
Kenneth O. StanleyResearch Manager, OpenAIAdresse e-mail validée de openai.com
