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While neuroevolution (evolving neural networks) has a successful track record across a variety of domains from reinforcement learning to artificial life, it is rarely applied to large, deep neural networks.
Artificial life
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Long short-term memory
Hochreiter, S. and Schmidhuber, J. (1997) · 1997
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Evolving artificial neural networks
Yao, X. (1999) · 1999
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Evolutionary Robotics
Nolfi, S. and Floreano, D. (2000) · 2000
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Evolution of digital organisms at high mutation rates leads to survival of the flattest
Wilke, C. O., Wang, J. L., Ofria, C., Lenski, R. E., and Adami, C. (2001) · 2001
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A survey of optimization by building and using probabilistic models
Pelikan, M., Goldberg, D. E., and Lobo, F. G. (2002) · 2002
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Evolving neural networks through augmenting topologies
Stanley, K. O. and Miikkulainen, R. (2002) · 2002
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Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation (cma-es)
Hansen, N., Müller, S. D., and Koumoutsakos, P. (2003) · 2003
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A taxonomy for artificial embryogeny
Stanley, K. O. and Miikkulainen, R. (2003) · 2003
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Self-adaptation in evolutionary algorithms
Meyer-Nieberg, S. and Beyer, H.-G. (2007) · 2007
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Natural evolution strategies
Wierstra, D., Schaul, T., Peters, J., and Schmidhuber, J. (2008) · 2008
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A hypercube-based indirect encoding for evolving large-scale neural networks
Stanley, K. O., D’Ambrosio, D. B., and Gauci, J. (2009) · 2009
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Understanding the difficulty of training deep feedforward neural networks
Glorot, X. and Bengio, Y. (2010) · 2010
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Deep sparse rectifier neural networks
Glorot, X., Bordes, A., and Bengio, Y. (2011) · 2011
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Abandoning objectives: Evolution through the search for novelty alone
Lehman, J. and Stanley, K. O. (2011) · 2011
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Enhancing es-hyperneat to evolve more complex regular neural networks
Risi, S. and Stanley, K. O. (2011) · 2011
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Coevolutionary Principles
Popovici, E., Bucci, A., Wiegand, R. P., and De Jong, E. D. (2012) · 2012
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Evolvability is inevitable: Increasing evolvability without the pressure to adapt
Lehman, J. and Stanley, K. O. (2013) · 2013
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Pygame learning environment
Tasfi, N. (2016) · 2016
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A wavelet-based encoding for neuroevolution
van Steenkiste, S., Koutník, J., Driessens, K., and Schmidhuber, J. (2016) · 2016
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Zagoruyko, S. and Komodakis, N. (2016) · 2016
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Pathnet: Evolution channels gradient descent in super neural networks
Fernando, C., Banarse, D., Blundell, C., Zwols, Y., Ha, D., Rusu, A. A., Pritzel, A., and Wierstra, D. (2017) · 2017
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Genetic policy optimization
Gangwani, T. and Peng, J. (2017) · 2017
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Adam: A method for stochastic optimization
Kingma, D. and Ba, J. (2014) · 2014
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Evolving deep unsupervised convolutional networks for vision-based reinforcement learning
Koutník, J., Schmidhuber, J., and Gomez, F. (2014) · 2014
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Illuminating search spaces by mapping elites
Mouret, J. and Clune, J. (2015) · 2015
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Trust region policy optimization
Schulman, J., Levine, S., Abbeel, P., Jordan, M., and Moritz, P. (2015) · 2015
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Deep learning
Goodfellow, I., Bengio, Y., and Courville, A. (2016) · 2016
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Learning to navigate in complex environments
Mirowski, P., Pascanu, R., Viola, F., Soyer, H., Ballard, A., Banino, A., Denil, M., Goroshin, R., Sifre, L., Kavukcuoglu, K., et al. (2016) · 2016
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Klambauer, G., Unterthiner, T., Mayr, A., and Hochreiter, S. (2017) · 2017
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ES is more than just a traditional finite-difference approximator
Lehman, J., Chen, J., Clune, J., and Stanley, K. O. (2017) · 2017
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Hierarchical representations for efficient architecture search
Liu, H., Simonyan, K., Vinyals, O., Fernando, C., and Kavukcuoglu, K. (2017) · 2017
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Miikkulainen, R., Liang, J., Meyerson, E., Rawal, A., Fink, D., Francon, O., Raju, B., Navruzyan, A., Duffy, N., and Hodjat, B. (2017) · 2017
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Petroski Such, F., Madhavan, V., Conti, E., Lehman, J., Stanley, K. O., and Clune, J. (2017) · 2017
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Evolution Strategies as a Scalable Alternative to Reinforcement Learning
Salimans, T., Ho, J., Chen, X., Sidor, S., and Sutskever, I. (2017) · 2017
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