Recent trends in the use of statistical tests for comparing swarm and evolutionary computing algorithms: Practical guidelines and a critical review

• Published in: Swarm and evolutionary computation Vol. 54; p. 100665
• Main Authors: Carrasco, J., García, S., Rueda, M.M., Das, S., Herrera, F.
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.05.2020
• Subjects: Bayesian; Non-parametric; Optimisation; Parametric; Statistical tests; Parametric; Non-parametric; Statistical tests; Optimisation; Bayesian
• ISSN: 2210-6502
• DOI: 10.1016/j.swevo.2020.100665

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. •Description of the classic and new trends in statistical analysis and their relation.•Tutorial on the use of statistical tests with code snippets and R shiny application.•Practical examples in the context of the CEC′17 evolutionary optimisation competition.