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h5 指数:39
h5 中位数:57

h5 指数是指在过去整整 5 年中所发表文章的 h 指数。h 指在 2018-2022 年间发表的 h 篇文章每篇至少都被引用过 h 次的最大值。隐藏

出版物的 h5 中位数，是指出版物的 h5 指数所涵盖的所有文章获得的引用次数的中位值。隐藏

#5 Evolutionary Computation

标题/作者	引用次数	年份
A genetic programming approach to designing convolutional neural network architectures. M Suganuma, S Shirakawa, T Nagao GECCO, 497-504	519	2017
NSGA-Net: neural architecture search using multi-objective genetic algorithm. Z Lu, I Whalen, V Boddeti, YD Dhebar, K Deb, ED Goodman, W Banzhaf GECCO, 419-427	214	2019
Evolving mario levels in the latent space of a deep convolutional generative adversarial network. V Volz, J Schrum, J Liu, SM Lucas, AM Smith, S Risi GECCO, 221-228	173	2018
Particle swarm optimization for hyper-parameter selection in deep neural networks. PR Lorenzo, J Nalepa, M Kawulok, LS Ramos, JR Pastor GECCO, 481-488	164	2017
GenAttack: practical black-box attacks with gradient-free optimization. M Alzantot, Y Sharma, S Chakraborty, H Zhang, CJ Hsieh, MB Srivastava GECCO, 1111-1119	156	2019
AlphaStar: an evolutionary computation perspective K Arulkumaran, A Cully, J Togelius Proceedings of the Genetic and Evolutionary Computation Conference Companion …	148	2019
Fast genetic algorithms. B Doerr, HP Le, R Makhmara, TD Nguyen GECCO, 777-784	138	2017
Where are we now?: a large benchmark study of recent symbolic regression methods. P Orzechowski, WG La Cava, JH Moore GECCO, 1183-1190	105	2018
Evolutionary architecture search for deep multitask networks. JZ Liang, E Meyerson, R Miikkulainen GECCO, 466-473	105	2018
PonyGE2: grammatical evolution in Python M Fenton, J McDermott, D Fagan, S Forstenlechner, E Hemberg, M O'Neill Proceedings of the Genetic and Evolutionary Computation Conference Companion …	86	2017
Autostacker: a compositional evolutionary learning system. B Chen, H Wu, W Mo, I Chattopadhyay, H Lipson GECCO, 402-409	77	2018
Large scale evolution of convolutional neural networks using volunteer computing T Desell Proceedings of the Genetic and Evolutionary Computation Conference Companion …	76	2017
ES is more than just a traditional finite-difference approximator. J Lehman, J Chen, J Clune, KO Stanley GECCO, 450-457	76	2018
Safe mutations for deep and recurrent neural networks through output gradients. J Lehman, J Chen, J Clune, KO Stanley GECCO, 117-124	75	2018
Evolutionary neural AutoML for deep learning. JZ Liang, E Meyerson, B Hodjat, D Fink, K Mutch, R Miikkulainen GECCO, 401-409	67	2019
Reference point specification in hypervolume calculation for fair comparison and efficient search. H Ishibuchi, R Imada, Y Setoguchi, Y Nojima GECCO, 585-592	64	2017
Hyper-parameter selection in deep neural networks using parallel particle swarm optimization PR Lorenzo, J Nalepa, LS Ramos, JR Pastor Proceedings of the Genetic and Evolutionary Computation Conference Companion …	63	2017
Neuroevolution of self-interpretable agents. Y Tang, D Nguyen, D Ha GECCO, 414-424	60	2020
Per instance algorithm configuration of CMA-ES with limited budget. N Belkhir, J Dréo, P Savéant, M Schoenauer GECCO, 681-688	59	2017
Talakat: bullet hell generation through constrained map-elites. A Khalifa, S Lee, A Nealen, J Togelius GECCO, 1047-1054	57	2018

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