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# Repeat steps 2-4 until satisfactory algorithm performance is reached or algorithm performance is no longer improving
Evolutionary optimization has been used in hyperparameter optimization for statistical machine learning algorithms,<ref name="bergstra11" /> [[automated machine learning]], typical neural network <ref name="kousiouris1"> {{cite journal | vauthors = Kousiouris G., Cuccinotta T., Varvarigou T. | year = 2011 | title= The effects of scheduling, workload type and consolidation scenarios on virtual machine performance and their prediction through optimized artificial neural networks | url= https://www.sciencedirect.com/science/article/abs/pii/S0164121211000951 | journal = Journal of Systems and Software | volume = 84 | issue = 8 | pages = 1270-1291}}</ref> and [[Deep learning#Deep neural networks|deep neural network]] architecture search,<ref name="miikkulainen1">{{cite arxiv | vauthors = Miikkulainen R, Liang J, Meyerson E, Rawal A, Fink D, Francon O, Raju B, Shahrzad H, Navruzyan A, Duffy N, Hodjat B | year = 2017 | title = Evolving Deep Neural Networks |eprint=1703.00548| class = cs.NE }}</ref><ref name="jaderberg1">{{cite arxiv | vauthors = Jaderberg M, Dalibard V, Osindero S, Czarnecki WM, Donahue J, Razavi A, Vinyals O, Green T, Dunning I, Simonyan K, Fernando C, Kavukcuoglu K | year = 2017 | title = Population Based Training of Neural Networks |eprint=1711.09846| class = cs.LG }}</ref> as well as training of the weights in deep neural networks.<ref name="such1">{{cite arxiv | vauthors = Such FP, Madhavan V, Conti E, Lehman J, Stanley KO, Clune J | year = 2017 | title = Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning |eprint=1712.06567| class = cs.NE }}</ref>
=== Population-based ===
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