A survey of swarm and evolutionary computing approaches for deep learning
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Experimental results showed that circle chaotic map was the best chaotic map that significantly boosted the performance of WOA.
“The performance of CWOA was compared with WOA and ten other optimization algorithms, after which experimental results showed that circle chaotic map was the best chaotic map that significantly boosted the performance of WOA [240].”
From Applications and Challenges of Artificial Intelligence in Space Missions (2024) · cites this paper as [240]
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · see citing works
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155 · see citing works
Hinton, G., Deng, L., Yu, D., Dahl, G., Mohamed, A.-R., Jaitly, N., Senior, A., Vanhoucke, V., Nguyen, P., Sainath, T., et al. (2012) · IEEE Signal Processing Magazine · doi:10.1109/msp.2012.2205597 · Cited by 10,260 · cited 1× · see citing works
“…such problems DL currently provide the best solutions to many problems in image recognition, speech recognition, and natural language processing (Hinton et al. 2012a, b) The human experience cannot be explored in many fields where DL can be used e.g. health applications (Lu et al. 2017) DL…”
Zagoruyko, S., & Komodakis, N. (2016) · Procedings of the British Machine Vision Conference 2016 · doi:10.5244/c.30.87 · Cited by 7,244 · see citing works
Greff, K., Srivastava, R. K., Koutnik, J., Steunebrink, B. R., & Schmidhuber, J. (2017) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2016.2582924 · Cited by 6,955 · see citing works
Greedy layer-wise training of deep networks
Bengio, Y., Lamblin, P., Popovici, D., & Larochelle, H. (2007) · Advances in Neural Information Processing Systems 19 · doi:10.7551/mitpress/7503.003.0024 · Cited by 4,841 · see citing works
A learning algorithm for Boltzmann machines
Ackley, D. H., Hinton, G. E., & Sejnowski, T. J. (1985) · Cognitive Science · doi:10.1207/s15516709cog0901_7 · Cited by 3,402 · see citing works
Biswas, A., & Chandrakasan, A. P. (2018) · 2018 IEEE International Solid - State Circuits Conference - (ISSCC) · doi:10.1109/isscc.2018.8310397 · Cited by 337 · see citing works
Corne, D. W., Reynolds, A., & Bonabeau, E. (2012) · Handbook of Natural Computing · doi:10.1007/978-3-540-92910-9_48 · Cited by 39 · see citing works
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Applications and Challenges of Artificial Intelligence in Space Missions
Oche, P. A., Ewa, G. A., & Ibekwe, N. (2024) · IEEE Access · doi:10.1109/access.2021.3132500 · Cited by 68 · see citing works
“In [239], Darwish et al. proposed the hybridization of SI and evolutionary computing algorithms for DL and how these integration solve some problems in satellite health monitoring based on ML data mining.”
Background
- Authors
- Darwish, A., Hassanien, A. E., & Das, S.
- Affiliations
- Scientific Research Group in Egypt (EG)
- Cairo University (EG)
- Indian Statistical Institute (IN)
- Corresponding author
- Darwish, Ashraf
- Hassanien, Aboul Ella
- Das, Swagatam
- Venue
- Artificial Intelligence Review
- Published
- 2019-06-13
- Volume
- 53
- Issue
- 3
- Pages
- 1767-1812
- ISSN
- 0269-2821 (ISSN-L)
- 1573-7462
- OpenAlex
- W2951402759
- Cited by
- 200
- References
- 9
- Citations by year
- 2026: 15 · 2025: 34 · 2024: 32 · 2023: 29 · 2022: 29 · 2021: 37 · 2020: 19 · 2019: 5
- FWCI
- 10.38
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Artificial Intelligence Review (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Metaheuristic Optimization Algorithms Research
- Physical Sciences › Computer Science › Artificial Intelligence › Stochastic Gradient Optimization Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
- Keywords
- swarm intelligence, evolutionary computation, deep learning, hyperparameter optimization, deep neural network training, classification accuracy, big data analytics, non-convex optimization
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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