Evolution of neural networks

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[1]  Lawrence Davis,et al.  Training Feedforward Neural Networks Using Genetic Algorithms , 1989, IJCAI.

[2]  Risto Miikkulainen,et al.  Evolving Neural Networks through Augmenting Topologies , 2002, Evolutionary Computation.

[3]  Brad Fullmer and Risto Miikkulainen.  Using Marker-Based Genetic Encoding Of Neural Networks To Evolve Finite-State Behaviour , 1991 .

[4]  Risto Miikkulainen,et al.  Task decomposition with neuroevolution in extended predator-prey domain , 2012, ALIFE.

[5]  Elliot Meyerson,et al.  Evolving Deep Neural Networks , 2017, Artificial Intelligence in the Age of Neural Networks and Brain Computing.

[6]  Kenneth O. Stanley,et al.  ES is more than just a traditional finite-difference approximator , 2017, GECCO.

[7]  Elliot Meyerson,et al.  Evolutionary architecture search for deep multitask networks , 2018, GECCO.

[8]  Risto Miikkulainen,et al.  Regularized evolutionary population-based training , 2020, GECCO.

[9]  Risto Miikkulainen,et al.  Accelerated Neural Evolution through Cooperatively Coevolved Synapses , 2008, J. Mach. Learn. Res..

[10]  Risto Miikkulainen,et al.  Evolving agent behavior in multiobjective domains using fitness-based shaping , 2010, GECCO '10.

[11]  J. D. Schaffer,et al.  Combinations of genetic algorithms and neural networks: a survey of the state of the art , 1992, [Proceedings] COGANN-92: International Workshop on Combinations of Genetic Algorithms and Neural Networks.

[12]  Elliot Meyerson,et al.  Evaluating medical aesthetics treatments through evolved age-estimation models , 2021, GECCO.

[13]  X. Yao.  Evolving Artificial Neural Networks , 1999 .

[14]  Kenneth O. Stanley,et al.  Abandoning Objectives: Evolution Through the Search for Novelty Alone , 2011, Evolutionary Computation.

[15]  Risto Miikkulainen,et al.  Acquiring evolvability through adaptive representations , 2007, GECCO '07.

[16]  Risto Miikkulainen,et al.  Cultural enhancement of neuroevolution , 2002 .

[17]  Shimon Whiteson,et al.  Neuroevolutionary reinforcement learning for generalized control of simulated helicopters , 2011, Evol. Intell..

[18]  Christian Igel,et al.  Neuroevolution for reinforcement learning using evolution strategies , 2003, The 2003 Congress on Evolutionary Computation, 2003. CEC '03..

[19]  Kenneth O. Stanley,et al.  POET: open-ended coevolution of environments and their optimized solutions , 2019, GECCO.

[20]  Risto Miikkulainen,et al.  Evolutionary optimization of deep learning activation functions , 2020, GECCO.

[21]  Risto Miikkulainen,et al.  Enhanced optimization with composite objectives and novelty pulsation , 2019, GECCO.

[22]  L. D. Whitley,et al.  Genetic Reinforcement Learning for Neurocontrol Problems , 2004, Machine Learning.

[23]  R. Hanel,et al.  Scaling , 2018, Oxford Scholarship Online.

[24]  Risto Miikkulainen,et al.  Neuroannealing: martingale optimization for neural networks , 2013, GECCO '13.

[25]  Risto Miikkulainen,et al.  Evolutionary Bilevel Optimization for Complex Control Tasks , 2015, GECCO.

[26]  Risto Miikkulainen,et al.  Evolving symmetric and modular neural networks for distributed control , 2009, GECCO.

[27]  Danna Zhou,et al.  d. , 1840, Microbial pathogenesis.

[28]  Kalyanmoy Deb,et al.  A population-based fast algorithm for a billion-dimensional resource allocation problem with integer variables , 2017, Eur. J. Oper. Res..

[29]  Kenneth O. Stanley,et al.  Generative encoding for multiagent learning , 2008, GECCO '08.

[30]  Elliot Meyerson,et al.  Discovering evolutionary stepping stones through behavior domination , 2017, GECCO.

[31]  Xin Yao,et al.  Evolving artificial neural networks , 1999, Proc. IEEE.

[32]  Max Jaderberg,et al.  Population Based Training of Neural Networks , 2017, ArXiv.

[33]  Marco Colombetti,et al.  Incremental Robot Shaping , 1998, Connect. Sci..

[34]  Kenneth A. De Jong,et al.  Cooperative Coevolution: An Architecture for Evolving Coadapted Subcomponents , 2000, Evolutionary Computation.

[35]  L. Darrell Whitley,et al.  Adding Learning to the Cellular Development of Neural Networks: Evolution and the Baldwin Effect , 1993, Evolutionary Computation.

[36]  Risto Miikkulainen,et al.  A Taxonomy for Artificial Embryogeny , 2003, Artificial Life.

[37]  Kenneth O. Stanley,et al.  A novel generative encoding for exploiting neural network sensor and output geometry , 2007, GECCO '07.

[38]  Risto Miikkulainen,et al.  Forming Neural Networks Through Efficient and Adaptive Coevolution , 1997, Evolutionary Computation.

[39]  Shimon Whiteson,et al.  Evolutionary Function Approximation for Reinforcement Learning , 2006, J. Mach. Learn. Res..

[40]  Elliot Meyerson,et al.  The Traveling Observer Model: Multi-task Learning Through Spatial Variable Embeddings , 2021, ICLR.

[41]  Risto Miikkulainen,et al.  Evolving Reusable Neural Modules , 2004, GECCO.

[42]  Dario Floreano,et al.  Neuroevolution: from architectures to learning , 2008, Evol. Intell..

[43]  Peter J. Angeline,et al.  An evolutionary algorithm that constructs recurrent neural networks , 1994, IEEE Trans. Neural Networks.

[44]  Xi Chen,et al.  Evolution Strategies as a Scalable Alternative to Reinforcement Learning , 2017, ArXiv.

[45]  Risto Miikkulainen,et al.  Evolving Adaptive Poker Players for Effective Opponent Exploitation , 2017, AAAI Workshops.

[46]  Risto Miikkulainen,et al.  Human-Like Combat Behaviour via Multiobjective Neuroevolution , 2012, Believable Bots.

[47]  Risto Miikkulainen,et al.  Constructing competitive and cooperative agent behavior using coevolution , 2010, CIG.

[48]  Elliot Meyerson,et al.  Effective reinforcement learning through evolutionary surrogate-assisted prescription , 2020, GECCO.

[49]  Magnus Thor Jonsson,et al.  Evolution and design of distributed learning rules , 2000, 2000 IEEE Symposium on Combinations of Evolutionary Computation and Neural Networks. Proceedings of the First IEEE Symposium on Combinations of Evolutionary Computation and Neural Networks (Cat. No.00.

[50]  Risto Miikkulainen,et al.  Incremental Evolution of Complex General Behavior , 1997, Adapt. Behav..

[51]  Risto Miikkulainen,et al.  Efficient evolution of neural networks through complexification , 2004 .

[52]  Risto Miikkulainen,et al.  Evolving adaptive neural networks with and without adaptive synapses , 2003, The 2003 Congress on Evolutionary Computation, 2003. CEC '03..

[53]  Risto Miikkulainen,et al.  Evolving Keepaway Soccer Players through Task Decomposition , 2003, GECCO.

[54]  Kenneth O. Stanley,et al.  A Case Study on the Critical Role of Geometric Regularity in Machine Learning , 2008, AAAI.

[55]  H. P. de Vladar.  Why Greatness Cannot Be Planned: The Myth of the Objective , 2016, Leonardo.

[56]  Risto Miikkulainen,et al.  Evolving Soccer Keepaway Players Through Task Decomposition , 2005, Machine Learning.

[57]  Dario Floreano,et al.  Evolutionary robots with on-line self-organization and behavioral fitness , 2000, Neural Networks.

[58]  Hod Lipson,et al.  Distilling Free-Form Natural Laws from Experimental Data , 2009, Science.

[59]  R. Miikkulainen,et al.  Learning Behavior Characterizations for Novelty Search , 2016, GECCO.

[60]  Xin Yao,et al.  Evolutionary ensembles with negative correlation learning , 2000, IEEE Trans. Evol. Comput..

[61]  Risto Miikkulainen,et al.  Acquiring Visibly Intelligent Behavior with Example-Guided Neuroevolution , 2007, AAAI.

[62]  Risto Miikkulainen,et al.  Population-Based Training for Loss Function Optimization , 2020, ArXiv.

[63]  Holger H. Hoos,et al.  Programming by optimization , 2012, Commun. ACM.

[64]  Karl Sims,et al.  Evolving 3D Morphology and Behavior by Competition , 1994, Artificial Life.

[65]  Chen Liang,et al.  AutoML-Zero: Evolving Machine Learning Algorithms From Scratch , 2020, ICML.

[66]  Stefano Nolfi,et al.  Good teaching inputs do not correspond to desired responses in ecological neural networks , 1994, Neural Processing Letters.

[67]  Risto Miikkulainen,et al.  Efficient credit assignment through evaluation function decomposition , 2005, GECCO '05.

[68]  Risto Miikkulainen,et al.  Open-ended behavioral complexity for evolved virtual creatures , 2013, GECCO '13.

[69]  Risto Miikkulainen,et al.  Creative AI Through Evolutionary Computation: Principles and Examples , 2019, SN Computer Science.

[70]  Risto Miikkulainen,et al.  Evolving neural network ensembles for control problems , 2005, GECCO '05.

[71]  Risto Miikkulainen,et al.  Trading control intelligence for physical intelligence: muscle drives in evolved virtual creatures , 2014, GECCO.

[72]  Risto Miikkulainen,et al.  Optimizing loss functions through multi-variate taylor polynomial parameterization , 2020, GECCO.

[73]  Kenneth O. Stanley,et al.  Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning , 2017, ArXiv.

[74]  Alok Aggarwal,et al.  Regularized Evolution for Image Classifier Architecture Search , 2018, AAAI.

[75]  David H. Sharp,et al.  Scaling, machine learning, and genetic neural nets , 1989 .

[76]  Samy Bengio,et al.  Show and tell: A neural image caption generator , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[77]  Risto Miikkulainen,et al.  Accelerating evolution via egalitarian social learning , 2012, GECCO '12.

[78]  Quoc V. Le,et al.  Evolving Normalization-Activation Layers , 2020, NeurIPS.

[79]  Elliot Meyerson,et al.  Modular Universal Reparameterization: Deep Multi-task Learning Across Diverse Domains , 2019, NeurIPS.

[80]  Richard K. Belew,et al.  Evolution, Learning, and Culture: Computational Metaphors for Adaptive Algorithms , 1990, Complex Syst..

[81]  Elliot Meyerson,et al.  Evolutionary neural AutoML for deep learning , 2019, GECCO.

[82]  Risto Miikkulainen,et al.  Robust non-linear control through neuroevolution , 2003 .

[83]  Elliot Meyerson,et al.  Pseudo-task Augmentation: From Deep Multitask Learning to Intratask Sharing - and Back , 2018, ICML.

[84]  Simon M. Lucas,et al.  A comparison of matrix rewriting versus direct encoding for evolving neural networks , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).