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Titlebook: Experimental Methods for the Analysis of Optimization Algorithms; Thomas Bartz-Beielstein,Marco Chiarandini,Mike Pre Book 2010 Springer-Ve

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https://doi.org/10.1007/978-3-319-06874-9le experimental designs. The example is a component-wise analysis of local search algorithms for the 2-edge-connectivity augmentation problem. We use standard statistical software to perform the analysis and report the R commands. Data sets and the analysis in SAS are available in an online compendium.
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On Applications of Extreme Value Theory in Optimizationd the performances of the procedures investigated, analytically and by simulations. In particular, we find that the estimated extreme value distributions and the fit to the outcomes characterize the performance of the optimizer in one single instance.
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Mixed Models for the Analysis of Optimization Algorithmsle experimental designs. The example is a component-wise analysis of local search algorithms for the 2-edge-connectivity augmentation problem. We use standard statistical software to perform the analysis and report the R commands. Data sets and the analysis in SAS are available in an online compendium.
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Geomagnetic Diagnosis of the Magnetosphereen lead to a full distributional characterization. This approach to the experimental assessment and comparison of MO performance is based on statistical inference methodology, in particular, estimation and hypothesis testing.
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The Future of Experimental Researchn experimental perspective. Therefore, we make use of the sequential parameter optimization (SPO) method that has been successfully applied as a tuning procedure to numerous heuristics for practical and theoretical optimization problems.
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