[
Albadr, M.A., Tiun, S., Ayob, M. and Al-Dhief, F. (2020). Genetic algorithm based on natural selection theory for optimization problems, Symmetry 12(11): 1758.
]Search in Google Scholar
[
Aszemi, N.M. and Dominic, D.D. (2019). Hyperparameter optimization in convolutional neural network using genetic algorithms, International Journal of Advanced Computer Science and Applications 10(6): 269–278.
]Search in Google Scholar
[
Aydogdu, M.F., Celik, V. and Demirci, M.F. (2017). Comparison of three different CNN architectures for age classification, 11th IEEE International Conference on Semantic Computing (ICSC), San Diego, USA, pp. 372–377.
]Search in Google Scholar
[
Bacanin, N., Zivkovic, M., Salb, M., Strumberger, I. and Chhabra, A. (2021). Convolutional neural networks hyperparameters optimization using sine cosine algorithm, in S. Shakya et al. (Eds), Sentimental Analysis and Deep Learning: Proceedings of ICSADL 2021, Springer, Singapore, pp. 863–878.
]Search in Google Scholar
[
Bergstra, J. and Bengio, Y. (2012). Random search for hyper-parameter optimization, Journal of Machine Learning Research 13(2): 281–305.
]Search in Google Scholar
[
Betró, B. (1991). Bayesian methods in global optimization, Journal of Global Optimization 1: 1–14.
]Search in Google Scholar
[
Bibaeva, V. (2018). Using metaheuristics for hyper-parameter optimization of convolutional neural networks, IEEE 28th International Workshop on Machine Learning for Signal Processing (MLSP), Aalborg, Denmark, pp. 1–6.
]Search in Google Scholar
[
Chhabra, Y., Varshney, S. and Ankita (2017). Hybrid particle swarm training for convolution neural network, International Conference on Contemporary Computing, Noida, India, pp. 1–3.
]Search in Google Scholar
[
Cui, H., Bai, J. (2019). A new hyperparameters optimization method for convolutional neural networks, Pattern Recognition Letters 125: 828–834.
]Search in Google Scholar
[
Dargan, S., Kumar, M., Ayyagari, M.R. and Kumar, G. (2020). A survey of deep learning and its applications: A new paradigm to machine learning, Archives of Computational Methods in Engineering 27: 1071–1092.
]Search in Google Scholar
[
Gadekallu, T.R., Alazab, M., Kaluri, R., Maddikunta, P.K.R., Bhattacharya, S., Lakshmanna, K. and Parimala, M. (2021). Hand gesture classification using a novel CNN-crow search algorithm, Complex & Intelligent Systems 7: 1855–1868.
]Search in Google Scholar
[
Guo, G. and Zhang, N. (2019). A survey on deep learning based face recognition, Computer Vision and Image Understanding 189: 102805.
]Search in Google Scholar
[
Hassanat, A., Almohammadi, K., Alkafaween, E., Abunawas, E., Hammouri, A. and Prasath, V.S. (2019). Choosing mutation and crossover ratios for genetic algorithms—A review with a new dynamic approach, Information 10(12): 390.
]Search in Google Scholar
[
Ioffe, S. and Szegedy, C. (2015). Batch normalization: Accelerating deep network training by reducing internal covariate shift, International Conference on Machine Learning, Lille, France, pp. 448–456.
]Search in Google Scholar
[
Karlupia, N., Sambyal, P., Abrol, P. and Lehana, P. (2019). BFO and GA based optimization of illumination switching patterns in large establishments, 6th International Conference on Computing for Sustainable Global Development (INDIACom), New Delhi, India, pp. 349–354.
]Search in Google Scholar
[
Li, Y. and Abdallah., S. (2020). On hyperparameter optimization of machine learning algorithms: Theory and practice, Neurocomputing 415: 295–316.
]Search in Google Scholar
[
Liu, J. and An, F.-P. (2020). Image classification algorithm based on deep learning-kernel function, Scientific Programming 3: 1–14.
]Search in Google Scholar
[
Mahajan, P., Abrol, P. and Lehana, P.K. (2020a). Effect of blurring on identification of aerial images using convolution neural networks, in P.K. Singh et al. (Eds.), proceedings of ICRIC 2019: Recent Innovations in Computing, Springer, Cham, pp. 469–484.
]Search in Google Scholar
[
Mahajan, P., Abrol, P., Lehana, P.K. (2020b). Scene based classification of aerial images using convolution neural networks, Journal of Scientific and Industrial Research 79(12): 1087–1094.
]Search in Google Scholar
[
Mahajan, P., Jakhetiya, V., Abrol, P., Lehana, P., Subudhi, B.N. and Guntuku, S.C. (2021). Perceptual quality evaluation of hazy natural images, IEEE Transactions on Industrial Informatics 17(12): 8046–8056.
]Search in Google Scholar
[
Mahajan, P., Karlupia, N., Abrol, P. and Lehana, P.K. (2021). Identifying COVID-19 pneumonia using chest radiography using deep convolutional neural networks, 62nd International Scientific Conference on Information Technology and Management Science of Riga Technical University (ITMS), Riga, Latvia, pp. 1–6.
]Search in Google Scholar
[
Mohakud, R. and Dash, R. (2021). Designing a grey wolf optimization based hyper-parameter optimized convolutional neural network classifier for skin cancer detection, Journal of King Saud University: Computer and Information Sciences 34(8): 1319–1578.
]Search in Google Scholar
[
Patro, K.K., Prakash, A.J., Samantray, S., Pławiak, J., Tadeusiewicz, R. and Pławiak, P. (2022). A hybrid approach of a deep learning technique for real-time ECG beat detection, International Journal of Applied Mathematics and Computer Science 32(3): 455–465, DOI: 10.34768/amcs-2022-0033.
]Open DOISearch in Google Scholar
[
Pujol, F.A., Mora, H. and Girona-Selva, J.A. (2016). A connectionist computational method for face recognition, International Journal of Applied Mathematics and Computer Science 26(2): 451–465, DOI: 10.1515/amcs-2016-0032.
]Open DOISearch in Google Scholar
[
Raju, K., Chinna Rao, B., Saikumar, K. and Lakshman Pratap, N. (2022). An optimal hybrid solution to local and global facial recognition through machine learning, in P. Kumar et al. (Eds), A Fusion of Artificial Intelligence and Internet of Things for Emerging Cyber Systems, Springer, Cham, pp. 203–226.
]Search in Google Scholar
[
Rodrigues, L.F., Backes, A.R., Travençolo, B.A.N. and de Oliveira, G.M.B. (2022). Optimizing a deep residual neural network with genetic algorithm for acute lymphoblastic leukemia classification, Journal of Digital Imaging 35(3): 623–637.
]Search in Google Scholar
[
Rojas, R. (1996). Neural Networks: A Systematic Introduction, Springer, Berlin/Heidelberg.
]Search in Google Scholar
[
Sikha, O. and Bharath, B. (2022). VGG16-random Fourier hybrid model for masked face recognition, Soft Computing 26(22): 12795–12810.
]Search in Google Scholar
[
Suganuma, M., Shirakawa, S. and Nagao, T. (2018). A genetic programming approach to designing convolutional neural network architectures, International Joint Conference on Artificial Intelligence, Stockholm, Sweden, pp. 5369–5373.
]Search in Google Scholar
[
Syulistyo, A.R., Purnomo, D.M.J., Rachmadi, M.F. and Wibowo, A. (2016). Particle swarm optimization (PSO) for training optimization on convolutional neural network (CNN), Complex & Intellelligent Systems 9(1): 52–58.
]Search in Google Scholar
[
Victoria, H. and Maragatham, G. (2021). Automatic tuning of hyperparameters using Bayesian optimization, Evolving Systems 12: 217–223.
]Search in Google Scholar
[
Vose, A., Balma, J., Heye, A., Rigazzi, A., Siegel, C., Moise, D., Robbins, B. and Sukumar, S. R. (2019). Recombination of artificial neural networks, ArXiv: abs/1901.03900.
]Search in Google Scholar
[
Wu, J., Chen, X.-Y., Zhang, H., Xiong, L.-D., Lei, H. and Deng, S.-H. (2019). Hyperparameter optimization for machine learning models based on bayesian optimization, Journal of Electronic Science and Technology 17(1): 26–40.
]Search in Google Scholar
[
Yoo, J.-H., Yoon, H.-i., Kim, H.-G., Yoon, H.-S. and Han, S.-S. (2019). Optimization of hyper-parameter for CNN model using genetic algorithm, 2019 1st International Conference on Electrical, Control and Instrumentation Engineering (ICECIE), Kuala Lumpur, Malaysia, pp. 1–6.
]Search in Google Scholar
[
Wang, Y., Zhang, H. and Zhang, G. (2019). CPSO-CNN: An efficient PSO-based algorithm for fine-tuning hyper-parameters of convolutional neural networks, Swarm and Evolutionary Computation 49: 114–123.
]Search in Google Scholar
[
Zhou, M. (2021). Heuristic hyperparameter optimization for convolutional neural networks using genetic algorithm, arXiv: 2112.07087.
]Search in Google Scholar
