The DOI matches its Crossref record; 66 of the 66 references it cites were found in a registry or an index.
Let a biogeography-based optimizer train your Multi-Layer Perceptron
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Citations
Cites
Multilayer feedforward networks are universal approximators
Hornik, K., Stinchcombe, M., & White, H. (1989) · Neural Networks · doi:10.1016/0893-6080(89)90020-8 · Cited by 21,728
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
Individual Comparisons by Ranking Methods
Wilcoxon, F. (1945) · Biometrics Bulletin · doi:10.2307/3001968 · Cited by 15,354
No free lunch theorems for optimization
Wolpert, D. H., & Macready, W. G. (1997) · IEEE Transactions on Evolutionary Computation · doi:10.1109/4235.585893 · Cited by 14,237
Extreme learning machine: Theory and applications
Huang, G.-B., Zhu, Q.-Y., & Siew, C.-K. (2006) · Neurocomputing · doi:10.1016/j.neucom.2005.12.126 · Cited by 13,285
UCI Repository of machine learning databases
Blake, C. L. (1998) · Medical Entomology and Zoology · Cited by 10,582
Multilayer feedforward networks are universal approximators
HornikK., StinchcombeM., & WhiteH. (1989) · Neural Networks · doi:10.5555/70405.70408 · Cited by 9,081
Training feedforward networks with the Marquardt algorithm
Hagan, M. T., & Menhaj, M. B. (1994) · IEEE Transactions on Neural Networks · doi:10.1109/72.329697 · Cited by 7,701
Derrac, J., García, S., Molina, D., & Herrera, F. (2011) · Swarm and Evolutionary Computation · doi:10.1016/j.swevo.2011.02.002 · Cited by 5,512
Cited by
A novel hybrid GWO-SCA approach for optimization problems
Singh, N., & Singh, S. B. (2017) · Engineering Science and Technology, an International Journal · doi:10.1016/j.jestch.2017.11.001 · Cited by 214
Binary biogeography-based optimization based SVM-RFE for feature selection
Albashish, D., Hammouri, A. I., Braik, M., Atwan, J., & Sahran, S. (2021) · Applied Soft Computing · doi:10.1016/j.asoc.2020.107026 · Cited by 154
Passive sonar target classification using multi-layer perceptron trained by salp swarm algorithm
Khishe, M., & Mohammadi, H. (2019) · Ocean Engineering · doi:10.1016/j.oceaneng.2019.04.013 · Cited by 108
Metadata
- Authors
- Mirjalili, S., Mirjalili, S. M., & Lewis, A.
- Venue
- Information Sciences
- Publisher
- Elsevier BV
- Published
- 2014-06
- Volume
- 269
- Pages
- 188-209
- ISSN
- 0020-0255 (ISSN-L)
- OpenAlex
- W2033731173
- Cited by
- 329
- References
- 66
- FWCI
- 26.74
- Type
- journal article
- Language
- en
- Open access
- closed
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- Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Let a biogeography-based optimizer train your Multi-Layer Perceptron. Information Sciences. https://doi.org/10.1016/j.ins.2014.01.038
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.
