Deep Statistical Comparison of Meta-heuristic Stochastic Optimization Algorithms
T. Eftimov, P. Korošec , B. Koroušić Seljak
the Genetic and Evolutionary Computation Conference Companion GECCO 2018
, Kyoto, Japan, 15-19 July, 2018
In this paper a recently proposed approach for making a statistical comparison of meta-heuristic stochastic optimization algorithms is presented. The main contribution of this approach is that the ranking scheme is based on the whole distribution, instead of using only one statistic to describe the distribution, such as average or median. Experimental results showed that our approach gives more robust results compared to state-of-the-art approaches in case when the results are affected by outliers or by statistical insignificant differences that could exist between data values.
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