Computer Science ›› 2023, Vol. 50 ›› Issue (3): 254-265.doi: 10.11896/jsjkx.220600007
• Artificial Intelligence • Previous Articles Next Articles
HU Zhongyuan, XUE Yu, ZHA Jiajie
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| [1]WANG X W,LI J,TAN Z H,et al.The state of the art and future tendency of “Internet+” oriented network technology[J].Journal of Computer Research and Development,2016,53(4):729-741. [2]HAO L,SHAO Z P,ZHANG J H,et al.Review of deep lear-ning-based action recognition algorithm[J].Computer Science,2020,47(S1):139-147. [3]KAVIANI S,SOHN I.Influence of random topology in artificial neural networks:A survey[J].ICT Express,2020,6(2):145-150. [4]LIPTON Z C,BERKOWITZ J,ELKAN C.A critical review of recurrent neural networks for sequence learning[J].arXiv:1506.00019,2015. [5]CHE Z,PURUSHOTHAM S,CHO K,et al.Recurrent neural networks for multivariate time series with missing values[J].Scientific reports,2018,8(1):1-12. [6]MIKOLOV T,KARAFIÁT M,BURGET L,et al.Recurrentneural network based language model[C]//Interspeech.2010:1045-1048. [7]KALCHBRENNER N,BLUNSOM P.Recurrent continuoustranslation models[C]//Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing.2013:1700-1709. [8]YOU Q,JIN H,WANG Z,et al.Image captioning with semantic attention[C]//Proceedings of the IEEE Conference on Compu-ter Vision and Pattern Recognition.2016:4651-4659. [9]WERBOS P J.Backpropagation through time:what it does and how to do it[C]//Proceedings of the IEEE.1990:1550-1560. [10]BIANCHINI M,GORI M,MAGGINI M.On the problem oflocal minima in recurrent neural networks[J].IEEE Transactions on Neural Networks,1994,5(2):167-177. [11]PASCANU R,MIKOLOV T,BENGIO Y.On the difficulty of training recurrent neural networks[C]//International Confe-rence on Machine Learning.PMLR,2013:1310-1318. [12]YAO Q,WANG M,CHEN Y,et al.Taking human out of lear-ning applications:A survey on automated machine learning[J].arXiv:1810.13306,2018. [13]SPARKS E R,TALWALKAR A,HAAS D,et al.Automating model search for large scale machine learning[C]//Proceedings of the Sixth ACM Symposium on Cloud Computing.2015:368-380. [14]SCHRIMPF M,MERITY S,BRADBURY J,et al.A flexible approach to automated rnn architecture generation[J].arXiv:1712.07316,2017. [15]ZOPH B,LE Q V.Neural architecture search with reinforce-ment learning[J].arXiv:1611.01578,2016. [16]PHAM H,GUAN M,ZOPH B,et al.Efficient neural architecture search via parameters sharing[C]//International Confe-rence on Machine Learning.PMLR,2018:4095-4104. [17]BACK T,HAMMEL U,SCHWEFEL H P.Evolutionary computation:Comments on the history and current state[J].IEEE Transactions on Evolutionary Computation,1997,1(1):3-17. [18]KOU G J,MA Y Y,YUE J,et al.Survey of bio-inspired natural computing[J].Computer Science,2014,41(S1):37-41. [19]KATOCH S,CHAUHAN S S,KUMAR V.A review on genetic algorithm:past,present,and future[J].Multimedia Tools and Applications,2021,80(5):8091-8126. [20]DAS S,SUGANTHAN P N.Differential evolution:A survey of the state-of-the-art[J].IEEE Transactions on Evolutionary Computation,2010,15(1):4-31. [21]KENNEDY J,EBERHART R.Particle swarm optimization[C]//International Conference on Neural Networks(ICNN’95).1995:1942-1948. [22]KRASNOGOR N,SMITH J.A tutorial for competent memetic algorithms:model,taxonomy,and design issues[J].IEEE transactions on Evolutionary Computation,2005,9(5):474-488. [23]ZHOU A,QU B Y,LI H,et al.Multiobjective evolutionary algorithms:A survey of the state of the art[J].Swarm and evolutionary computation,2011,1(1):32-49. [24]YAO X,CHEN G L,XU H M,et al.A survey of evolutionary algorithms[J].Chinese Journal of Computers,1995(9):694-706. [25]XIE X,LIU Y,SUN Y,et al.BenchENAS:A BenchmarkingPlatform for Evolutionary Neural Architecture Search[J].IEEE Transactions on Evolutionary Computation,2022,26(6):1473-1485. [26]ELMAN J L.Finding structure in time[J].Cognitive science,1990,14(2):179-211. [27]KARPATHY A.The unreasonable effectiveness of recurrentneural networks[EB/OL].(2015-05-21) [2016-08-13].http://karpathy.github.io/2015/05/21 /rnn-effectiveness,2016. [28]BAHDANAU D,CHO K,BENGIO Y.Neural machine translation by jointly learning to align and translate[J].arXiv:1409.0473,2014. [29]HOCHREITER S,SCHMIDHUBER J.Long short-term memory[J].Neural computation,1997,9(8):1735-1780. [30]JAEGER H,HAAS H.Harnessing nonlinearity:Predictingchaotic systems and saving energy in wireless communication[J].Science,2004,304(5667):78-80. [31]CHUNG J,GULCEHRE C,CHO K H,et al.Empirical evaluation of gated recurrent neural networks on sequence modeling[J].arXiv:1412.3555,2014. [32]LECUN Y,BENGIO Y,HINTON G.Deep learning [J].Nature,2015,521(7553):436-444. [33]GRAVES A,FERNÁNDEZ S,SCHMIDHUBER J.Bidirectional LSTM networks for improved phoneme classification and recognition[C]//International Conference on Artificial Neural Networks.2005:799-804. [34]GRAVES A,MOHAMED A,HINTON G.Speech recognition with deep recurrent neural networks[C]//2013 IEEE International Conference on Acoustics,Speech and Signal Processing.2013:6645-6649. [35]MA L B,LI N,CHENG S.Evolutionary neural networks:principles,models and methods[J].Journal of Shaanxi Normal University(Natural Science Edtion),2021,49(5):30-38,133. [36]GE H W,LIANG Y C,MARCHESE M.A modified particle swarm optimization-based dynamic recurrent neural network for identifying and controlling nonlinear systems[J].Computers & structures,2007,85(21/22):1611-1622. [37]CAI X,ZHANG N,VENAYAGAMOORTHY G K,et al.Time series prediction with recurrent neural networks trained by a hybrid PSO-EA algorithm[J].Neurocomputing,2007,70(13/14/15):2342-2353. [38]XIAO P,VENAYAGAMOORTHY G K,CORZINE K A.Combined training of recurrent neural networks with particle swarm optimization and backpropagation algorithms for impedance identification[C]//2007 IEEE Swarm Intelligence Symposium.2007:9-15. [39]ZHANG N,BEHERA P K,WILLIAMS C.Solar radiation prediction based on particle swarm optimization and evolutionary algorithm using recurrent neural networks[C]//2013 IEEE International Systems Conference(SysCon).IEEE,2013:280-286. [40]KANG Q,LIAO W K,AGRAWAL A,et al.A hybrid training algorithm for recurrent neural network using particle swarm optimization-based preprocessing and temporal error aggregation[C]//2017 IEEE International Conference on Data Mining Workshops(ICDMW).IEEE,2017:812-817. [41]IBRAHIM A M,EL-AMARY N H.Particle Swarm Optimization trained recurrent neural network for voltage instability prediction[J].Journal of Electrical Systems and Information Technology,2018,5(2):216-228. [42]HU H,WANG H,BAI Y,et al.Determination of endometrial carcinoma with gene expression based on optimized Elman neural network[J].Applied Mathematics and Computation,2019,341:204-214. [43]AB AZIZ M F,MOSTAFA S A,FOOZY C F M,et al.Integrating Elman recurrent neural network with particle swarm optimization algorithms for an improved hybrid training of multidisciplinary datasets[J].Expert Systems with Applications,2021,183:115441. [44]DUONG S C,UEZATO E,KINJO H,et al.A hybrid evolutio-nary algorithm for recurrent neural network control of a three-dimensional tower crane[J].Automation in Construction,2012,23:55-63. [45]BLANCO A,DELGADO M,PEGALAJAR M C.A real-coded genetic algorithm for training recurrent neural networks[J].Neural Networks,2001,14(1):93-105. [46]GUO C X,YUAN G,ZHU C Z,et al.SoC estimation for lithiumion battery using recurrent NARX neural network and genetic algorithm[C]//IOP Conference Series:Materials Science and Engineering.IOP Publishing,2019. [47]DUCHANOY C A,MORENO-ARMENDÁRIZ M A,URBINA L,et al.A novel recurrent neural network soft sensor via a differential evolution training algorithm for the tire contact patch[J].Neurocomputing,2017,235:71-82. [48]NAWI N M,KHAN A,REHMAN M Z,et al.Weight optimization in recurrent neural networks with hybrid metaheuristic Cuckoo search techniques for data classification[J].Mathematical Problems in Engineering,2021,18:115441. [49]RUÍZ L G B,CAPEL M I,PEGALAJAR M C.Parallel memetic algorithm for training recurrent neural networks for the energy efficiency problem[J].Applied Soft Computing,2019,76:356-368. [50]RASHID T A,FATTAH P,AWLA D K.Using accuracy mea-sure for improving the training of LSTM with metaheuristic algorithms[J].Procedia Computer Science,2018,140:324-333. [51]ROY K,MANDAL K K,MANDAL A C.Ant-Lion Optimizer algorithm and recurrent neural network for energy management of micro grid connected system[J].Energy,2019,167:402-416. [52]SHAH H,CHIROMA H,HERAWAN T,et al.An efficient bio-inspired bees colony for breast cancer prediction[C]//Proceedings of the International Conference on Data Engineering 2015(DaEng-2015).Singapore:Springer,2019:597-608. [53]YANG S,CHEN D,LI S,et al.Carbon price forecasting based on modified ensemble empirical mode decomposition and long short-term memory optimized by improved whale optimization algorithm[J].Science of the Total Environment,2020,716:137117. [54]HASSIB E,EL-DESOUKY A,LABIB L,et al.WOA+BRNN:An imbalanced big data classification framework using Whale optimization and deep neural network[J].Soft Computing,2020,24(8):5573-5592. [55]GROSAN C,ABRAHAM A.Hybrid evolutionary algorithms:methodologies,architectures,and reviews[M]//Hybrid evolutionary Algorithms.2007:1-17. [56]VICOL P,METZ L,SOHL-DICKSTEIN J.Unbiased gradient estimation in unrolled computation graphs with persistent evolution strategies[C]//International Conference on Machine Learning.PMLR,2021:10553-10563. [57]LI Y,ZHU Z,KONG D,et al.EA-LSTM:Evolutionary attention-based LSTM for time series prediction[J].Knowledge-Based Systems,2019,181:104785. [58]BERGSTRA J,BARDENET R,BENGIO Y,et al.Algorithms for hyper-parameter optimization[C]//International Conference on in Neural Information Processing Systems.2011. [59]LORENZO P R,NALEPA J,KAWULOK M,et al.Particleswarm optimization for hyper-parameter selection in deep neural networks[C]//Proceedings of the Genetic and Evolutionary Computation Conference.2017:481-488. [60]TANAKA T,MORIYA T,SHINOZAKI T,et al.Automated structure discovery and parameter tuning of neural network language model based on evolution strategy[C]//2016 IEEE Spoken Language Technology Workshop(SLT).IEEE,2016:665-671. [61]PENG L,LIU S,LIU R,et al.Effective long short-term memory with differential evolution algorithm for electricity price prediction[J].Energy,2018,162:1301-1314. [62]CHUNG H,SHIN K.Genetic algorithm-optimized long short-term memory network for stock market prediction[J].Sustai-nability,2018,10(10):1-18. [63]CAMERO A,TOUTOUH J,STOLFI D H,et al.Evolutionary deep learning for car park occupancy prediction in smart cities[C]//International Conference on Learning and Intelligent Optimization.Cham:Springer,2018:386-401. [64]LIU R,LIU L.Predicting housing price in China based on long short-term memory incorporating modified genetic algorithm[J].Soft Computing,2019,23(22):11829-11838. [65]CAMERO A,TOUTOUH J,FERRER J,et al.Waste gene-ration prediction under uncertainty in smart cities through deep neuroevolution[J].Revista Facultad de Ingeniería Universidad de Antioquia,2019(93):128-138. [66]CAMERO A,TOUTOUH J,ALBA E.A specialized evolu-tionary strategy using mean absolute error random sampling to design recurrent neural networks[J].arXiv:1909.02425,2019. [67]CHUNG C C,LIN W T,ZHANG R,et al.Emotion estimation by joint facial expression and speech tonality using evolutionary deep learning structures[C]//2019 IEEE 8th Global Conference on Consumer Electronics(GCCE).IEEE,2019:221-224. [68]ALMALAQ A,ZHANG J J.Evolutionary deep learning-based energy consumption prediction for buildings[J].IEEE Access,2018,7:1520-1531. [69]PENG L,ZHU Q,LV S X,et al.Effective long short-term me-mory with fruit fly optimization algorithm for time series forecasting[J].Soft Computing,2020,24(19):15059-15079. [70]BOUKTIF S,FIAZ A,OUNI A,et al.Multi-sequence LSTM-RNN deep learning and metaheuristics for electric load forecasting[J].Energies,2020,13(2):1-21. [71]ORTEGO P,DIEZ-OLIVAN A,DEL SER J,et al.Evolutionary LSTM-FCN networks for pattern classification in industrial processes[J].Swarm and Evolutionary Computation,2020,54:100650. [72]VIJAYAPRABAKARAN K,SATHIYAMURTHY K.Towards activation function search for long short-term model network:A differential evolution based approach[J].Journal of King Saud University-Computer and Information Sciences,2022,34,2637-2650. [73]NESHAT M,NEZHAD M M,ABBASNEJAD E,et al.An evolutionary deep learning method for short-term wind speed prediction:A case study of the lillgrund offshore wind farm[J].arXiv:2002.09106,2020. [74]SOMU N,MR G R,RAMAMRITHAM K.A hybrid model for building energy consumption forecasting using long short term memory networks[J].Applied Energy,2020,261:114131. [75]VISWAMBARAN R A,CHEN G,XUE B,et al.Evolving deep recurrent neural networks using a new variable-length genetic algorithm[C]//2020 IEEE Congress on Evolutionary Computation(CEC).IEEE,2020:1-8. [76]DIEZ-OLIVAN A,ORTEGO P,DEL SER J,et al.Adaptivedendritic cell-deep learning approach for industrial prognosis under changing conditions[J].IEEE Transactions on Industrial Informatics,2021,17(11):7760-7770. [77]ROGERS B,NOMAN N,CHALUP S,et al.Evolutionary Hyperparameter Optimisation for Sentence Classification[C]//2021 IEEE Congress on Evolutionary Computation(CEC).IEEE,2021:958-965. [78]VIOLOS J,TSANAKAS S,THEODOROPOULOS T,et al.Hypertuming GRU Neural Networks for Edge Resource Usage Prediction[C]//2021 IEEE Symposium on Computers and Communications(ISCC).IEEE,2021:1-8. [79]PAWAR S A,SAN O,YEN G G.Hyperparameter Search using the Genetic Algorithm for Surrogate Modeling of Geophysical Flows[C]//AIAA SCITECH 2022 Forum.2022. [80]ANGELINE P J,SAUNDERS G M,POLLACK J B.An evolutionary algorithm that constructs recurrent neural networks[J].IEEE Transactions on Neural Networks,1994,5(1):54-65. [81]JUANG C F.A hybrid of genetic algorithm and particle swarm optimization for recurrent network design[J].IEEE Transactions on Systems,Man,and Cybernetics,Part B(Cybernetics),2004,34(2):997-1006. [82]BAYER J,WIERSTRA D,TOGELIUS J,et al.Evolving memory cell structures for sequence learning[C]//International Conference on Artificial Neural Networks.Berlin:Springer,2009:755-764. [83]WANG H,WANG H,XU K.Evolutionary recurrent neural network for image captioning[J].Neurocomputing,2020,401:249-256. [84]RAWAL A,MIIKKULAINEN R.Evolving deep LSTM-basedmemory networks using an information maxi-mization objective[C]//Proceedings of the Genetic and Evolutionary Computation Conference 2016.2016:501-508. [85]STANLEY K O,MIIKKULAINEN R.Evolving neural net-works through augmenting topologies[J].Evolutionary computation,2002,10(2):99-127. [86]RAWAL A,MIIKKULAINEN R.From nodes to networks:Evolving recurrent neural networks[J].arXiv:1803.04439,2018. [87]DESELL T,CLACHAR S,HIGGINS J,et al.Evolving deep recurrent neural networks using ant colony optimization[C]//European Conference on Evolutionary Computation in Combinatorial Optimization.2015:86-98. [88]ELSAID A E R,EL JAMIY F,HIGGINS J,et al.Optimizing long short-term memory recurrent neural networks using ant colony optimization to predict turbine engine vibration[J].Applied Soft Computing,2018,73:969-991. [89]ELSAID A E R,JAMIY F E,HIGGINS J,et al.Using ant colony optimization to optimize long short-term memory recurrent neural networks[C]//Proceedings of the Genetic and Evolutio-nary Computation Conference.2018:13-20. [90]ELSAID A E R A,ORORBIA A G,DESELL T J.The ant swarm neuro-evolution procedure for optimizing recurrent networks[J].arXiv:1909.11849,2019. [91]ELSAID A E R,BENSON S,PATWARDHAN S,et al.Evolving recurrent neural networks for time series data prediction of coal plant parameters[C]//International Conference on the Applications of Evolutionary Computation(Part of EvoStar).Cham:Springer,2019:488-503. [92]ORORBIA A,ELSAID A E R,DESELL T.Investigating recurrent neural network memory structures using neuro-evolution[C]//Proceedings of the Genetic and Evolutionary Computation Conference.2019:446-455. [93]ORORBIA II A G,MIKOLOV T,REITTER D.Learning simpler language models with the differential state framework[J].Neural Computation,2017,29(12):3327-3352. [94]ZHOU G B,WU J,ZHANG C L,et al.Minimal gated unit for recurrent neural networks[J].International Journal of Automation and Computing,2016,13(3):226-234. [95]COLLINS J,SOHL-DICKSTEIN J,SUSSILLO D.Capacity and trainability in recurrent neural networks[J].arXiv:1611.09913,2016. [96]ALBA E,TOMASSINI M.Parallelism and evolution-ary algorithms[J].IEEE transactions on evolutionary computation,2002,6(5):443-462. [97]DESELL T,ELSAID A E R A,ORORBIA A G.Investigating Deep Recurrent Connections and Recurrent Memory Cells Using Neuro-Evolution[M]//Deep Neural Evolution.2020:253-291. [98]MO H,CUSTODE L L,IACCA G.Evolutionary neural architecture search for remaining useful life predic-tion[J].Applied Soft Computing,2021,108:107474. [99]CAMERO A,TOUTOUH J,ALBA E.Random error sampling-based recurrent neural network archi-tecture optimization[J].Engineering Applications of Artificial Intelligence,2020,96:103946. [100]LYU Z,ELSAID A E R,KARNS J,et al.An ExperimentalStudy of Weight Initialization and Weight Inheritance Effects on Neuroevolution[J].arXiv:2009.09644,2020. [101]HANSEN L K,SALAMON P.Neural network ensem-bles[J].IEEE transactions on pattern analysis and machine intelligence,1990,12(10):993-1001. [102]YAO W S,WAN Q,CHEN Z Q,et al.The researching overview of evolutionary neural networ-ks[J].Computer Science,2004(3):125-129. [103]SMITH C,JIN Y.Evolutionary multi-objective generation of recurrent neural network ensembles for time series prediction[J].Neurocomputing,2014,143:302-311. [104]AI S,CHAKRAVORTY A,RONG C.Evolutionary ensemblelstm based household peak demand prediction[C]//2019 International Conference on Artificial Intelligence in Information and Communication(ICAIIC).IEEE,2019:1-6. [105]VISWAMBARAN R A,CHEN G,XUE B,et al.Evolutionary design of long short term memory(lstm) ensemble[C]//2020 IEEE Symposium Series on Computational Intelligence(SSCI).IEEE,2020:2692-2698. [106]VISWAMBARAN R A,CHEN G,XUE B,et al.Two-Stage Genetic Algorithm for Designing Long Short Term Memory(LSTM) Ensembles[C]//2021 IEEE Congress on Evolutionary Computation(CEC).IEEE,2021:942-949. [107]PAN S J,YANG Q.A survey on transfer learning [J].IEEETransactions on Knowledge and Data Engineering,2009,22(10):1345-1359. [108]ZHUANG F,QI Z,DUAN K,et al.A comprehensive survey on transfer learning[C]//Proceedings of the IEEE.2020:43-76. [109]ELSAID A E R,KARNAS J,LYU Z,et al.Neuro-evolutionary transfer learning through structural adaptation[C]//International Conference on the Applications of Evolutionary Computation(Part of EvoStar).Cham:Springer,2020:610-625. [110]ELSAID A E R,KARNS J,ORORBIA II A,et al.Neuro-evolutionary Transfer Learning of Deep Recurrent Neural Networks through Network-Aware Adaptation[J].arXiv:2006.02655,2020. [111]ELSAID A E R,KARNS J,LYU Z,et al.Improving neuro-evolutionary transfer learning of deep recurrent neural networks through network-aware adaptation [C]//Proceedings of the 2020 Genetic and Evolutionary Computation Conference.2020:315-323. |
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