gfo-project / Gradient-Free-Optimizers

What it does

Designed for hyperparameter tuning, simulation optimization, feature selection, engineering design, and any scenario where gradients are unavailable or impractical. The library prioritizes simplicity: define your objective function, specify the search space, and run. All algorithms share one consistent API, so switching from hill climbing to Bayesian optimization is a one-line change. SciPy is optional; GFO only requires NumPy and pandas, making it suitable as an optimization backend or for minimal environments, containers, and embedded systems.