Algorithm data sets for the bbob-constrained test suite
In the table below, you will find all official algorithm data sets on the bbob-constrained 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.
| Algorithm | Year | Author(s) | Dataset | Comment |
|---|---|---|---|---|
| RandomSearch-5 | 2022 | Dufossé | tgz | source code |
| AL1-CMA-ES | 2022 | Dufossé and Atamna | tgz | CMA-ES with Augmented Lagrangian fitness, pycma version, parameter setting 1 as compared by Dufosse and Atamna for BBOB-2022 |
| AL2-CMA-ES | 2022 | Dufossé and Atamna | tgz | CMA-ES with Augmented Lagrangian fitness, pycma version, parameter setting 2 as compared by Dufosse and Atamna for BBOB-2022 |
| AL3-CMA-ES | 2022 | Dufossé and Atamna | tgz | CMA-ES with Augmented Lagrangian fitness, pycma version, parameter setting 3 (default) as compared by Dufosse and Atamna for BBOB-2022 |
| AL4-CMA-ES | 2022 | Dufossé and Atamna | tgz | CMA-ES with Augmented Lagrangian fitness, pycma version, parameter setting 4 as compared by Dufosse and Atamna for BBOB-2022 |
| BPepsMAg | 2022 | Hellwig and Beyer | tgz | Matrix Adaptation Evolution Strategy with restarts and BIPOP strategy, using up to three different constraint handling techniques, as compared by Hellwig and Beyer for BBOB-2022 |
| COBYLA | 2022 | Dufossé and Atamna | tgz | Constrained Optimization BY Linear Approximation (implemented in SciPy as a wrapper around Powell’s fortran code) compared by Dufosse and Atamna for BBOB-2022 |
| epsMAg | 2022 | Hellwig and Beyer | tgz | Matrix Adaptation Evolution Strategy, using up to three different constraint handling techniques, as compared by Hellwig and Beyer for BBOB-2022 |
| fmincon | 2022 | Hellwig and Beyer | tgz | default fmincon from Matlab2021b, as compared by Hellwig and Beyer for BBOB-2022 |