https://github.com/deephyper/cbbo-benchmarks
C-BBO benchmarks for DeepHyper
https://github.com/deephyper/cbbo-benchmarks
Last synced: 10 months ago
JSON representation
C-BBO benchmarks for DeepHyper
- Host: GitHub
- URL: https://github.com/deephyper/cbbo-benchmarks
- Owner: deephyper
- Created: 2025-03-31T19:42:14.000Z (11 months ago)
- Default Branch: main
- Last Pushed: 2025-04-15T19:36:59.000Z (11 months ago)
- Last Synced: 2025-04-15T20:24:19.066Z (11 months ago)
- Language: Python
- Homepage:
- Size: 66.4 KB
- Stars: 0
- Watchers: 3
- Forks: 0
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# C-BBO benchmarks
Continuous Black-Box Optimization (C-BBO) benchmarks for DeepHyper.
| Function Name | Number of Dimensions | Comment |
| ------------- | --------------------- | ------------------------------------------- |
| ackley | $\infty$ (default 5) | Many local minima and single global optimum |
| branin | 2 | Three global optimum |
| cossin | 1 | Many local minima, good for visualisation. |
| easom | 2 | Almost flat everywhere |
| griewank | $\infty$ (default 5) | |
| hartmann6D | 6 | |
| levy | $\infty$ (default 5) | |
| michal | $\infty$ (default 2) | |
| rosen | $\infty$ (default 5) | |
| schwefel | $\infty$ (default 5) | |
| shekel | 4 | Many local minima with flat areas |
## Installation
Python installation and dependency management is handled with uv. Clone this repository then create a Python environment with `uv sync`.
## Usage
Go to the `example` directory and run the benchmarks with `uv run benchmark cbbo.toml`. Plot the results of the benchmarks with `uv run benchmark cbbo.toml --plot`.