[1] D. Silver, David et al., "Mastering the Game of Go with Deep Neural Networks and Tree Search," Nature, vol. 529, pp. 484-489, DOI: 10.1038/nature16961, 2016.
[2] P. Vikhar, "Evolutionary Algorithms: A Critical Review and Its Future Prospects," Proc. of the IEEE Int. Conf. on Global Trends in Signal Process., Inf. Comp. and Comm. pp. 261-265, Jalgaon, India, 2016.
[3] F. Gomez, J. Schmidhuber and R. Miikkulainen, "Accelerated Neural Evolution through Cooperatively Coevolved Synapses," Journal of Machine Learning Research, vol. 9, pp. 937-965, 2008.
[4] R. De Nardi, J. Togelius, O. Holland and S. Lucas, "Evolution of Neural Networks for Helicopter Control: Why Modularity Matters," Proc. of the IEEE Int. Conf. on Evolutionary Computation, pp. 1799-1806, DOI: 10.1109/CEC.2006.1688525, Vancouver, Canada, 2006.
[5] V. Heidrich-Meisner, C. Igel, B. Hoeffding and Bernstein, "Races for Selecting Policies in Evolutionary Direct Policy Search," Proc. of the 26th Annual Int. Conf. on Machine Learning (ICML '09), vol. 51, DOI: 10.1145/1553374.1553426, 2009.
[6] J. Lehman et al., "The Surprising Creativity of Digital Evolution: A Collection of Anecdotes from the Evolutionary Computation and Artificial Life Research Communities," Massachusetts Institute of Technology, Artificial Life, vol. 26, no. 2, pp. 274–306, 2020.
[7] F. Such et al., "Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning," arXiv, DOI: 10.48550/arXiv.1712.06567, 2017.
[8] X. Zhang, J. Clune and K. Stanley, "On the Relationship between the OpenAI Evolution Strategy and Stochastic Gradient Descent," arXiv: 1712.06564, DOI: 10.48550/arXiv.1712.06564, 2017.
[9] J. Lehman, J. Chen, J. Clune and K. Stanley, "ES Is More Than Just a Traditional Finite-difference Approximator," Proc. of the Genetic and Evolutionary Computation Conference (GECCO '18), pp. 450- 457, DOI: 10.1145/3205455.3205474, 2018.
[10] E. Conti, Edoardo et al., "Improving Exploration in Evolution Strategies for Deep Reinforcement Learning via a Population of Novelty-seeking Agents," Proc. of the 32nd Int. Conf. on Neural Information Processing Systems (NIPS'18), pp. 5032–5043, 2017.
[11] J. Metzen, M. Edgington, Y. Kassahun and F. Kirchner, "Performance Evaluation of EANT in the Robocup Keepaway Benchmark," Proc. of the 6th Int. Conf. on Machine Learning and Applications (ICMLA 2007), pp. 342-347, DOI: 10.1109/ICMLA.2007.23, 2008.
[12] F. Gomez, J. Schmidhuber and R. Miikkulainen, "Accelerated Neural Evolution through Cooperatively Coevolved Synapses," JMLR, vol. 9, pp. 937-965, DOI: 10.1145/1390681.1390712, 2008.
[13] K. Stanley and R. Miikkulainen, "Evolving Neural Networks through Augmenting Topologies," Evolutionary Computation, vol. 10, pp. 99-127, DOI: 10.1162/106365602320169811, 2002.
[14] E. Real, A. Aggarwal, Y. Huang and Q. Le, "Regularized Evolution for Image Classifier Architecture Search," Proc. of AAAI Conf. on Artificial Intellig., vol. 33, DOI: 10.1609/aaai.v33i01.33014780, 2018.
[15] A. Gaier and D. Ha, "Weight Agnostic Neural Networks," arXiv: 1906.04358, DOI: 10.13140/RG.2.2.16025.88169, 2019.
[16] S. Hochreiter, Untersuchungen zu dynamischen neuronalen Netzen, Diploma Thesis, Josef Hochreiter Institut fur Informatik, Technische Universitat Munchen, Germany, 1991.
[17] F. Informatik, Y. Bengio, P. Frasconi and J. Schmidhuber Jfirgen, "Gradient Flow in Recurrent Nets: the Difficulty of Learning Long-Term Dependencies," Chapter of Book: A Field Guide to Dynamical Recurrent Neural Networks, pp. 237 – 243, DOI: 10.1109/9780470544037.ch14, IEEE Press, 2003.
[18] Y. Bengio, P. Simard and P. Frasconi, "Learning Long-term Dependencies with Gradient Descent Is Difficult," IEEE Transactions on Neural Networks, vol. 5, pp. 157-166, DOI: 10.1109/72.279181, 1994.
[19] R. Pascanu, T. Mikolov and Y. Bengio, "On the Difficulty of Training Recurrent Neural Networks," Proc. of the 30th Int. Conf. on Machine Learning, JMLR: W&CP, vol. 28, Atlanta, Georgia, USA, 2013.
[20] K. He, X. Zhang, S. Ren and J. Sun, "Deep Residual Learning for Image Recognition," Proc. of the IEEE Conf. on Comp. Vision and Pattern Recog. (CVPR), pp. 770-778, DOI: 10.1109 CVPR.2016.90, 2016.
[21] X. Glorot, A. Bordes and Y. Bengio, "Deep Sparse Rectifier Neural Networks," Proc. of the 14th Int. Conf. on Artificial Intelligence and Statistics, vol. 15, pp. 315-323, Fort Lauderdale, FL, USA, 2011.
[22] Y. Lecun, L. Bottou, G. Orr and K.-R. Müller, "Efficient BackProp," Chapter in Book: Neural Networks: Tricks of the Trade, vol. 7700, pp. 9-48, DOI: 10.1007\/3-540-49430-8\_2, 1998.
[23] X. Glorot and Y. Bengio, "Understanding the Difficulty of Training Deep Feedforward Neural Networks," Journal of Machine Learning Research, vol. 9, pp. 249-256, 2010.
[24] S. Ioffe and C. Szegedy, "Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift," arXiv: 1502.03167, DOI: 10.48550/arXiv.1502.03167, 2015.
[25] Y. Lecun et al., "Backpropagation Applied to Handwritten Zip Code Recognition," Neural Computation, vol. 1, pp. 541-551, DOI: 10.1162 neco.1989.1.4.541, 1989.
[26] H. Noh, T. You, J. Mun and B. Han, "Regularizing Deep Neural Networks by Noise: Its Interpretation and Optimization," Proc. of the 31st Conf. on Neural Inf. Process. Sys. (NIPS), Long Beach, USA, 2017.
[27] S. Enrique, J. Hare and M. Niranjan, "Deep Cascade Learning," IEEE Transactions on Neural Networks and Learning Systems, vol. 29, no. 11, pp. 5475 – 5485, DOI: 10.1109/TNNLS.2018.2805098, 2018.
[28] C. Shannon and W. Weaver, The Mathematical Theory of Communication, Note 78, p. 44, 1963.
[29] J. Schmidhuber, "Learning Complex, Extended Sequences Using the Principle of History Compression," Neural Computation, vol. 4, pp. 234-242, DOI: 10.1162/neco.1992.4.2.234, 1992.
[30] O. Granmo et al., "The Convolutional Tsetlin Machine," arXiv: 1905.09688v5, DOI: 10.48550/arXiv.190 5.09688, 2019.