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Experiments conducted on two large scale benchmark datasets verify the effectiveness of these two different kinds of features.
The method is tested in 4 medium and large scale benchmark case studies and the no-splits results are compared to literature solutions with and without splits.
The experimental results demonstrate that GPBO can provide better solution accuracy and convergence ability in both large scale benchmark functions and real-world optimization problem.
The performance of the proposed STLBO is firstly tested and verified on two low dimension decomposable problems and twelve large scale benchmark functions, then on the parameter identification of PEM fuel cell as well as solar cell models.
Extensive experiments are performed using eight large scale benchmark datasets including BRATS 2012 (image dataset and synthetic dataset), 2013 (image dataset and synthetic dataset), 2014, 2015 and ISLES (Ischemic stroke lesion segmentation) 2015 and 2017.
BAliBase [ 22, 29] was the first large scale benchmark dataset against which alignment programs were routinely assessed.
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Computational experiments on 32 existing large scale benchmarks, as well as on 20 new very large scale problem instances, demonstrate that the proposed method is fast, competitive and able to find high-quality solutions for problem instances with up to 20,000 customers within reasonable CPU times.
In (Dejaeger et al. 2012) the authors conducted a large scale benchmarking study using different types of techniques and analyzed aspects related with the selection of features.
In large scale benchmarking, the FINDSITEcomb LHM approach exhibited significant performance advantages over traditional approaches in terms of enrichment factor, speed, and insensitivity as to whether experimental or predicted protein structures are used [12].
Most structure-based MHC prediction algorithms are not available online on the web, making large scale benchmarking of their predictive performance impossible.
Finally, larger scale benchmarking experiments will be conducted, testing the SDR in large networking configurations, such as the one available in the FP7 project CREW [26].
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com