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310 CHAPTER 15 Evolving Deep Neural Networks
[12] K.O. Stanley, R. Miikkulainen, Evolving neural networks through augmenting
topologies, Evolutionary Computation 10 (2002) 99e127.
[13] D. Floreano, P. Durr, C. Mattiussi, Neuroevolution: from architectures to learning,
Evolutionary Intelligence 1 (2008) 47e62.
[14] J. Lehman, R. Miikkulainen, Neuroevolution, Scholarpedia 8 (6) (2013) 30977.
[15] D.J. Montana, L. Davis, Training feedforward neural networks using genetic algo-
rithms, in: Proceedings of the 11th International Joint Conference on Artificial
Intelligence, Morgan Kaufmann, San Francisco, 1989, pp. 762e767.
[16] X. Yao, Evolving artificial neural networks, Proceedings of the IEEE 87 (9) (1999)
1423e1447.
[17] C. Igel, Neuroevolution for reinforcement learning using evolution strategies, in: Pro-
ceedings of the 2003 Congress on Evolutionary Computation, IEEE Press, Piscataway,
NJ, 2003, pp. 2588e2595.
[18] F. Gomez, R. Miikkulainen, Incremental evolution of complex general behavior, Adap-
tive Behavior 5 (1997) 317e342.
[19] F. Gomez, J. Schmidhuber, R. Miikkulainen, Accelerated neural evolution through
cooperatively coevolved synapses, Journal of Machine Learning Research 9 (2008)
937e965.
[20] D.E. Moriarty, R. Miikkulainen, Forming neural networks through efficient and adap-
tive coevolution, Evolutionary Computation 5 (1997) 373e399.
[21] F. Gruau, D. Whitley, Adding learning to the cellular development of neural networks:
evolution and the Baldwin effect, Evolutionary Computation 1 (1993) 213e233.
[22] G.E. Hinton, S.J. Nowlan, How learning can guide evolution, Complex Systems 1
(1987) 495e502.
[23] A. Sinha, P. Malo, P. Xu, K. Deb, A bilevel optimization approach to automated param-
eter tuning, in: Proceedings of the Genetic and Evolutionary Computation Conference
(GECCO 2014), Vancouver, BC, Canada, July 2014, 2014.
[24] P. Abbeel, A. Coates, M. Quigley, A.Y. Ng, An application of reinforcement learning to
aerobatic helicopter flight, in: Advances in Neural Information Processing Systems 19,
2007.
[25] J. Bagnell, J. Schneider, Autonomous helicopter control using reinforcement learning
policy search methods, in: Proceedings of the International Conference on Robotics
and Automation 2001, IEEE, May 2001.
[26] A.Y. Ng, H.J. Kim, M. Jordan, S. Sastry, Autonomous helicopter flight via reinforce-
ment learning, in: Advances in Neural Information Processing Systems 16, 2004.
[27] R. Koppejan, S. Whiteson, Neuroevolutionary reinforcement learning for generalized
control of simulated helicopters, Evolutionary Intelligence 4 (2011) 219e241.
[28] J.Z. Liang, R. Miikkulainen, Evolutionary bilevel optimization for complex control
tasks, in: Proceedings of the Genetic and Evolutionary Computation Conference
(GECCO 2015), Madrid, Spain, July 2015, 2015.
[29] I. Loshchilov, F. Hutter, CMA-ES for Hyperparameter Optimization of Deep Neural
Networks, 2016. ArXiv 1604.07269.
[30] C. Fernando, D. Banarse, F. Besse, M. Jaderberg, D. Pfau, M. Reynolds, M. Lactot,
D. Wierstra, Convolution by evolution: differentiable pattern producing networks, in:
Proceedings of the Genetic and Evolutionary Computation Conference (GECCO
2016), Denver, CO, 2016, 2016.
[31] K.O. Stanley, Compositional pattern producing networks: a novel abstraction of
development, Genetic Programming and Evolvable Machines 8 (June 2007) 131e162.