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• W2096166399 abstract "The interest in nonparametric statistical analysis has grown recently in the field of computational intelligence. In many experimental studies, the lack of the required properties for a proper application of parametric procedures–independence, normality, and homoscedasticity–yields to nonparametric ones the task of performing a rigorous comparison among algorithms. In this paper, we will discuss the basics and give a survey of a complete set of nonparametric procedures developed to perform both pairwise and multiple comparisons, for multi-problem analysis. The test problems of the CEC’2005 special session on real parameter optimization will help to illustrate the use of the tests throughout this tutorial, analyzing the results of a set of well-known evolutionary and swarm intelligence algorithms. This tutorial is concluded with a compilation of considerations and recommendations, which will guide practitioners when using these tests to contrast their experimental results." @default.
• W2096166399 created "2016-06-24" @default.
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• W2096166399 date "2011-03-01" @default.
• W2096166399 modified "2023-10-18" @default.
• W2096166399 title "A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms" @default.
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• W2096166399 doi "https://doi.org/10.1016/j.swevo.2011.02.002" @default.
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