Kenneth O. Stanley

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Joel LehmanOpenAIVerified email at openai.com
Risto MiikkulainenProfessor of Computer Science, the University of Texas at AustinVerified email at cs.utexas.edu
Jeff CluneAssociate Professor of Computer Science, U. British Columbia; Faculty Member, Vector InstituteVerified email at ubc.ca
Sebastian RisiProfessor, IT University of CopenhagenVerified email at itu.dk
David B. D'AmbrosioGoogleVerified email at eecs.ucf.edu
Jason GauciEngineering Manager, FacebookVerified email at fb.com
Lisa B. SorosRoman Family Teaching and Research Fellow, Barnard CollegeVerified email at barnard.edu
Justin K. PughUniversity of Central FloridaVerified email at cs.ucf.edu
Paul SzerlipGraduate Research Assistant, University of Central FloridaVerified email at knights.ucf.edu
Bobby D. BryantUniversity of Nevada, RenoVerified email at unr.edu
Amy K. HooverAssistant Professor of Informatics at New Jersey Institute of TechnologyVerified email at njit.edu
Julian TogeliusAssociate Professor of Computer Science and Engineering, New York University; co-founder, modl.aiVerified email at nyu.edu
Georgios N. YannakakisProfessor, Director, Inst. of Digital Games, University of Malta | modl.aiVerified email at um.edu.mt
Igor V. KarpovGraduate Student, The University of Texas at AustinVerified email at cs.utexas.edu
Charles OfriaMichigan State UniversityVerified email at cse.msu.edu
Andrea SoltoggioComputer Science Department, Loughborough UniversityVerified email at lboro.ac.uk
Phillip VerbancsicsGoogleVerified email at knights.ucf.edu
Joseph ReisingerThe University of Texas at AustinVerified email at cs.utexas.edu
Charlie HughesProfessor of Computer Science, University of Central FloridaVerified email at ucf.edu
Bryan WilderSchmidt Science Fellow, Carnegie Mellon University & Harvard School of Public HealthVerified email at andrew.cmu.edu

Kenneth O. Stanley

Research Manager, OpenAI

Verified email at openai.com - Homepage

Artificial Intelligence Neuroevolution Neural Networks Evolutionary Computation Artificial Life


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Evolving neural networks through augmenting topologies KO Stanley, R Miikkulainen Evolutionary computation 10 (2), 99-127, 2002	3820	2002
Abandoning objectives: Evolution through the search for novelty alone J Lehman, KO Stanley Evolutionary computation 19 (2), 189-223, 2011	933	2011
A hypercube-based encoding for evolving large-scale neural networks KO Stanley, DB D'Ambrosio, J Gauci Artificial life 15 (2), 185-212, 2009	905	2009
Search-based procedural content generation: A taxonomy and survey J Togelius, GN Yannakakis, KO Stanley, C Browne IEEE Transactions on Computational Intelligence and AI in Games 3 (3), 172-186, 2011	777	2011
Compositional pattern producing networks: A novel abstraction of development KO Stanley Genetic programming and evolvable machines 8 (2), 131-162, 2007	765	2007
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	657	2017
Exploiting open-endedness to solve problems through the search for novelty. J Lehman, KO Stanley ALIFE, 329-336, 2008	584	2008
A taxonomy for artificial embryogeny KO Stanley, R Miikkulainen Artificial life 9 (2), 93-130, 2003	583	2003
Efficient reinforcement learning through evolving neural network topologies KO Stanley, R Miikkulainen Proceedings of the 4th Annual Conference on genetic and evolutionary …, 2002	545*	2002
Competitive coevolution through evolutionary complexification KO Stanley, R Miikkulainen Journal of artificial intelligence research 21, 63-100, 2004	531	2004
Real-time neuroevolution in the NERO video game KO Stanley, BD Bryant, R Miikkulainen IEEE transactions on evolutionary computation 9 (6), 653-668, 2005	502	2005
Designing neural networks through neuroevolution KO Stanley, J Clune, J Lehman, R Miikkulainen Nature Machine Intelligence 1 (1), 24-35, 2019	461	2019
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	380	2011
Efficient reinforcement learning through evolving neural network topologies KO Stanley, R Miikkulainen Proceedings of the Genetic and Evolutionary Computation Conference (GECCO …, 2002	370	2002
Efficient reinforcement learning through evolving neural network topologies KO Stanley, R Miikkulainen Proceedings of the Genetic and Evolutionary Computation Conference (GECCO …, 2002	370	2002
Quality diversity: A new frontier for evolutionary computation JK Pugh, LB Soros, KO Stanley Frontiers in Robotics and AI, 40, 2016	336	2016
Improving exploration in evolution strategies for deep reinforcement learning via a population of novelty-seeking agents E Conti, V Madhavan, F Petroski Such, J Lehman, K Stanley, J Clune Advances in neural information processing systems 31, 2018	290	2018
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	275	2019
Efficient evolution of neural networks through complexification KO Stanley University of Texas at Austin, 2004	266	2004
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	249	2020

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