2008
DOI: 10.1007/s10732-008-9080-4
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A study on the use of non-parametric tests for analyzing the evolutionary algorithms’ behaviour: a case study on the CEC’2005 Special Session on Real Parameter Optimization

Abstract: In recent years, there has been a growing interest for the experimental analysis in the field of evolutionary algorithms. It is noticeable due to the existence of numerous papers which analyze and propose different types of problems, such as the basis for experimental comparisons of algorithms, proposals of different methodologies in comparison or proposals of use of different statistical techniques in algorithms' comparison.In this paper, we focus our study on the use of statistical techniques in the analysis… Show more

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Cited by 1,529 publications
(654 citation statements)
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“…Moreover, non parametric statistical tests were conducted in order to further elaborate the algorithms results. These are the Friedman test and the Wilcoxon signed-rank test, which are commonly used for the performance evaluation of EAs [60,61]. Both tests are conducted based on the obtained PAPR values, which are summarized in Tables 4 and 5.…”
Section: Simulation Resultsmentioning
confidence: 99%
“…Moreover, non parametric statistical tests were conducted in order to further elaborate the algorithms results. These are the Friedman test and the Wilcoxon signed-rank test, which are commonly used for the performance evaluation of EAs [60,61]. Both tests are conducted based on the obtained PAPR values, which are summarized in Tables 4 and 5.…”
Section: Simulation Resultsmentioning
confidence: 99%
“…Table 2 shows the characteristics of the used 22 datasets from the UCI [20]. Non-parametric tests have been used to compare the results of different algorithms or instances [21]. Specifically, we have considered two alternative methods to analyse the experimental results:…”
Section: Methodsmentioning
confidence: 99%
“…FeSLiC on the other hand, while it uses this stage conservatively for low to medium feature spaces, it relies on the simplification stage to an extortionate degree for high-dimensional datasets, in order to correct the inability of the rule extraction algorithm to locate the minimum number of informative features. For example, the average number features per [51][52] . The SGERD algorithm has been excluded from this analysis, because it could not be executed in the last three datasets.…”
Section: Comparative Analysismentioning
confidence: 99%