The DOI matches its Crossref record; 37 of the 37 references it cites were found in a registry or an index.
How effective is the Grey Wolf optimizer in training multi-layer perceptrons
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Cites
Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014) · Advances in Engineering Software · doi:10.1016/j.advengsoft.2013.12.007 · Cited by 19,343
Self-citationA logical calculus of the ideas immanent in nervous activity
McCulloch, W. S., & Pitts, W. (1943) · The Bulletin of Mathematical Biophysics · doi:10.1007/bf02478259 · Cited by 18,566
UCI Repository of machine learning databases
Blake, C. L. (1998) · Medical Entomology and Zoology · Cited by 10,582
Kohonen, T. (1990) · Proceedings of the IEEE · doi:10.1109/5.58325 · Cited by 8,414
Introduction to the theory of neural computation
(1994) · Neural Networks · doi:10.1016/0893-6080(94)90014-0 · Cited by 6,401
Evolutionary programming made faster
Yao, X., Liu, Y., & Lin, G. (1999) · IEEE Transactions on Evolutionary Computation · doi:10.1109/4235.771163 · Cited by 3,910
Evolution strategies – A comprehensive introduction
Beyer, H.-G., & Schwefel, H.-P. (2002) · Natural Computing · doi:10.1023/a:1015059928466 · Cited by 2,790
An empirical comparison of supervised learning algorithms
Caruana, R., & Niculescu-Mizil, A. (2006) · Proceedings of the 23rd international conference on Machine learning - ICML '06 · doi:10.1145/1143844.1143865 · Cited by 2,780
Dowsland, K. A., & Thompson, J. M. (2012) · Handbook of Natural Computing · doi:10.1007/978-3-540-92910-9_49 · Cited by 2,432
Simulated Annealing: Theory and Applications
van Laarhoven, P. J. M., & Aarts, E. H. L. (1987) · Springer eBooks · doi:10.1007/978-94-015-7744-1 · Cited by 2,385
Cited by
A novel hybrid PSO–GWO algorithm for optimization problems
Şenel, F. A., Gökçe, F., Yüksel, A. S., & Yiğit, T. (2019) · Engineering with Computers · doi:10.1007/s00366-018-0668-5 · Cited by 298
An efficient hybrid multilayer perceptron neural network with grasshopper optimization
Heidari, A. A., Faris, H., Aljarah, I., & Mirjalili, S. (2019) · Soft Computing · doi:10.1007/s00500-018-3424-2 · Cited by 283
Self-citation
Metadata
- Authors
- Mirjalili, S.
- Venue
- Applied Intelligence
- Published
- 2015-01-17
- Volume
- 43
- Issue
- 1
- Pages
- 150-161
- ISSN
- 0924-669X (ISSN-L)
- 1573-7497
- OpenAlex
- W2002302337
- Cited by
- 677
- References
- 37
- FWCI
- 42.86
- Type
- journal article
- Language
- en
- Open access
- closed
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- Mirjalili, S. (2015). How effective is the Grey Wolf optimizer in training multi-layer perceptrons. Applied Intelligence. https://doi.org/10.1007/s10489-014-0645-7
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
