The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms
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Citations
Cites
Handbook of Mathematical Functions
McQuarrie, D. A. (1966) · American Journal of Physics · doi:10.1119/1.1972842 · Cited by 40,433
Use of Ranks in One-Criterion Variance Analysis
Kruskal, W. H., & Wallis, W. A. (1952) · Journal of the American Statistical Association · doi:10.1080/01621459.1952.10483441 · Cited by 13,912
The use of ranks to avoid the assumption of normality implicit in the analysis of variance
Friedman, M. (1937) · Journal of the American Statistical Association · doi:10.1080/01621459.1937.10503522 · Cited by 7,005
A Comparison of Alternative Tests of Significance for the Problem of $m$ Rankings
Friedman, M. (1940) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177731944 · Cited by 2,785
Handbook of Parametric and Nonparametric Statistical Procedures: Third Edition
Sheskin, D. J. (2003) · doi:10.1201/9781420036268 · Cited by 1,551
Cited by
Mirjalili, S. (2016) · Neural Computing and Applications · doi:10.1007/s00521-015-1920-1 · Cited by 2,765
A social learning particle swarm optimization algorithm for scalable optimization
Cheng, R., & Jin, Y. (2015) · Information Sciences · doi:10.1016/j.ins.2014.08.039 · Cited by 747
Coyote optimization algorithm: A new metaheuristic for global optimization problems
Pierezan, J., & Dos Santos Coelho, L. (2018) · 2018 IEEE Congress on Evolutionary Computation (CEC) · doi:10.1109/cec.2018.8477769 · Cited by 645
Overview of Friedman’s test and post-hoc analysis
Pereira, D. G., Afonso, A., & Medeiros, F. M. (2015) · Communications in Statistics - Simulation and Computation · doi:10.1080/03610918.2014.931971 · Cited by 585
Chaotic whale optimization algorithm
Kaur, G., & Arora, S. (2018) · Journal of Computational Design and Engineering · doi:10.1016/j.jcde.2017.12.006 · Cited by 533
Metadata
- Authors
- Derrac, J., García, S., Molina, D., & Herrera, F.
- Venue
- Swarm and Evolutionary Computation
- Publisher
- Elsevier BV
- Published
- 2011-03
- Volume
- 1
- Issue
- 1
- Pages
- 3-18
- ISSN
- 2210-6502 (ISSN-L)
- OpenAlex
- W2096166399
- Cited by
- 5,512
- References
- 5
- FWCI
- 106.74
- Type
- journal article
- Language
- en
- Open access
- closed
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- The paper
- Derrac, J., García, S., Molina, D., & Herrera, F. (2011). A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm and Evolutionary Computation. https://doi.org/10.1016/j.swevo.2011.02.002
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.
