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.According to Pesarin [63], P is a non-parametric family of distributions if it is not possible to find a finite-dimensional space Θ in which there is a one-to-one relationship between Θ and P. This means that we do not have to assume that the underlying distribution belongs to a known family of distributions. Consequently, the prerequisites for non-parametric tests such as symmetry or 9

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