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In the table below, you will find all official algorithm data sets on the bbob-largescale test suite, together with their year of publication, the authors, and related PDFs for each data set. Links to the source code to run the corresponding experiments/algorithms are provided whenever available.
To sort the table, simply click on the table header of the corresponding column.
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| Number | Algorithm Name | Year | Author(s) | link to data | related PDFs, source code, and remarks |
|---|---|---|---|---|---|
| largescale-000 | CMA | 2019 | Varelas | [data]({{ page.dataDir }}/2019/CMA_Varelas_largescale.tgz) | GECCO 2019 paper |
| largescale-001 | LBFGS | 2019 | Varelas | [data]({{ page.dataDir }}/2019/LBFGS_Varelas_largescale.tgz) | GECCO 2019 paper |
| largescale-002 | LMCMA14 | 2019 | Varelas | [data]({{ page.dataDir }}/2019/LMCMA14_Varelas_largescale.tgz) | GECCO 2019 paper |
| largescale-003 | LMCMA17 | 2019 | Varelas | [data]({{ page.dataDir }}/2019/LMCMA17_Varelas_largescale.tgz) | GECCO 2019 paper |
| largescale-004 | R2ES | 2019 | Varelas | [data]({{ page.dataDir }}/2019/R2ES_Varelas_largescale.tgz) | GECCO 2019 paper |
| largescale-005 | R10ES | 2019 | Varelas | [data]({{ page.dataDir }}/2019/R10ES_Varelas_largescale.tgz) | GECCO 2019 paper |
| largescale-006 | V2D-CMA | 2019 | Varelas | [data]({{ page.dataDir }}/2019/V2D-CMA_Varelas_largescale.tgz) | GECCO 2019 paper |
| largescale-007 | VD-CMA | 2019 | Varelas | [data]({{ page.dataDir }}/2019/VD-CMA_Varelas_largescale.tgz) | GECCO 2019 paper |
| largescale-008 | VkD-CMA | 2019 | Varelas | [data]({{ page.dataDir }}/2019/VkD-CMA_Varelas_largescale.tgz) | GECCO 2019 paper |
| largescale-009 | m2DLBFGS | 2019 | Varelas | [data]({{ page.dataDir }}/2019/m2DLBFGS_Varelas_largescale.tgz) | GECCO 2019 paper |
| largescale-010 | sepCMA | 2019 | Varelas | [data]({{ page.dataDir }}/2019/sepCMA_Varelas_largescale.tgz) | GECCO 2019 paper |
| largescale-011 | BSrr | 2022 | Tanabe | [data]({{ page.dataDir }}/2022/BSrr_Tanabe.tgz) | Brent-STEP applied to single variables in a round-robin fashion: BBOB-2022 paper |
| largescale-012 | HJ-5 | 2022 | Tanabe | [data]({{ page.dataDir }}/2022/HJ-5_Tanabe.tgz) | Hooke-Jeeves with parameter c set to 0.5: BBOB-2022 paper |
| largescale-013 | HJ-9 | 2022 | Tanabe | [data]({{ page.dataDir }}/2022/HJ-9_Tanabe.tgz) | Hooke-Jeeves with parameter c set to 0.9: BBOB-2022 paper |
| largescale-014 | MTSLS1-5 | 2022 | Tanabe | [data]({{ page.dataDir }}/2022/MTSLS1-5_Tanabe.tgz) | Multiple Trajectory Search with local search LS1 and parameter c set to 0.5: BBOB-2022 paper |
| largescale-015 | MTSLS1-9 | 2022 | Tanabe | [data]({{ page.dataDir }}/2022/MTSLS1-9_Tanabe.tgz) | Multiple Trajectory Search with local search LS1 and parameter c set to 0.9: BBOB-2022 paper |
| largescale-016 | RANDOMSEARCH | 2024 | Brockhoff | [data]({{ page.dataDir }}/2024/RANDOMSEARCH_Brockhoff.zip) | continuous submission: uniform sampling in |