Embedded Bandits for Large-Scale Black-Box Optimization
Authors: Abdullah Al-Dujaili, S. Suresh
AAAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Furthermore, numerical experiments were conducted to validate its performance. The results show a clear performance gain over recently proposed random embedding methods for large-scale problems, provided the intrinsic dimensionality is low. |
| Researcher Affiliation | Collaboration | Abdullah Al-Dujaili,1 S. Suresh1,2 1 School of Computer Science and Engineering, NTU, Singapore 2 ST Engineering NTU Corporate Lab, Singapore |
| Pseudocode | Yes | Algorithm 1 The EMBEDDEDHUNTER Algorithm |
| Open Source Code | Yes | The code/data/supplemental materials of this paper will be made available at the project s website: http://ash-aldujaili.github.io/eh-lsopt. |
| Open Datasets | Yes | the Ellipsoid, Fletcher Powell, Rosenbrock, and Ackley test functions (Molga and Smutnicki 2005) |
| Dataset Splits | No | The paper evaluates algorithms on mathematical test functions rather than traditional datasets with explicit train/validation/test splits. It specifies experiment configurations like the number of function evaluations and dimensionality ranges, but not data partitioning for training and validation sets in a typical machine learning sense. |
| Hardware Specification | No | The paper mentions that algorithms were implemented in Python and test functions imported from a Python package but provides no details on the specific hardware used for experiments. |
| Software Dependencies | No | The compared algorithms were implemented in Python and the test functions were imported from the Optproblems Python package (Wessing 2016). While Python and the Optproblems package are mentioned, no specific version numbers for either are provided, preventing full reproducibility of the software environment. |
| Experiment Setup | Yes | Table 1: Experiments setup. Unless specified above, we set v = 104, n = 104, d = 10, and M = 5. The search space Y for RESOO and EMBEDDEDHUN T E R was set to [ d/η, d/η]d with η = 0.3, SRESOO s Y was set to [ 1, 1]d+1 as suggested in (Qian and Yu 2016; Qian, Hu, and Yu 2016), respectively. hmax was set to the square root of number of function evaluations for each tree. i.e., for RESOO and SRESOO, hmax = v/M; for EMBEDDEDHU N T E R, hmax = v. |