The DOI matches its Crossref record; 48 of the 48 references it cites were found in a registry or an index.
Recent trends in the use of statistical tests for comparing swarm and evolutionary computing algorithms: Practical guidelines and a critical review
Open accessAbstract
A key aspect of the design of evolutionary and swarm intelligence algorithms is studying their performance. Statistical comparisons are also a crucial part which allows for reliable conclusions to be drawn. In the present paper we gather and examine the approaches taken from different perspectives to summarise the assumptions made by these statistical tests, the conclusions reached and the steps followed to perform them correctly. In this paper, we conduct a survey on the current trends of the proposals of statistical analyses for the comparison of algorithms of computational intelligence and include a description of the statistical background of these tests. We illustrate the use of the most common tests in the context of the Competition on single-objective real parameter optimisation of the IEEE Congress on Evolutionary Computation (CEC) 2017 and describe the main advantages and drawbacks of the use of each kind of test and put forward some recommendations concerning their use.
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Cites
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Self-citationRank transformations as a bridge between parametric and nonparametric statistics
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Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms
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Scientists rise up against statistical significance
Amrhein, V., Greenland, S., & McShane, B. (2019) · Nature · doi:10.1038/d41586-019-00857-9 · Cited by 2,983
Moving to a World Beyond “ p < 0.05”
Wasserstein, R. L., Schirm, A. L., & Lazar, N. A. (2019) · The American Statistician · doi:10.1080/00031305.2019.1583913 · Cited by 2,976
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Self-citationSensitivity Analysis of k-Fold Cross Validation in Prediction Error Estimation
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Cited by
Górriz, J. M., Ramírez, J., Ortíz, A., Martínez-Murcia, F. J., Segovia, F., Suckling, J., Leming, M., Zhang, Y.-D., Álvarez-Sánchez, J. R., Bologna, G., et al. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.05.078 · Cited by 319
Self-citationMeta-heuristic as manager in federated learning approaches for image processing purposes
Połap, D., & Woźniak, M. (2021) · Applied Soft Computing · doi:10.1016/j.asoc.2021.107872 · Cited by 65
Ghosh, A., Das, S., Das, A. K., Senkerik, R., Viktorin, A., Zelinka, I., & Masegosa, A. D. (2022) · Swarm and Evolutionary Computation · doi:10.1016/j.swevo.2022.101057 · Cited by 56
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Metadata
- Authors
- Carrasco, J., García, S., Rueda, M. M., Das, S., & Herrera, F.
- Venue
- Swarm and Evolutionary Computation
- Publisher
- Elsevier BV
- Published
- 2020-05
- Volume
- 54
- Pages
- 100665
- ISSN
- 2210-6502 (ISSN-L)
- OpenAlex
- W3008773008
- Cited by
- 513
- References
- 48
- FWCI
- 28.22
- Type
- journal article
- Language
- en
- Open access
- green
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- Carrasco, J., García, S., Rueda, M. M., Das, S., & Herrera, F. (2020). Recent trends in the use of statistical tests for comparing swarm and evolutionary computing algorithms: Practical guidelines and a critical review. Swarm and Evolutionary Computation. https://doi.org/10.1016/j.swevo.2020.100665
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