SimonBlanke/Gradient-Free-Optimizers
Lightweight optimization with local, global, population-based and sequential techniques across mixed search spaces
When you need to find the best settings for a system, simulation, or machine learning model but can't use traditional calculus methods, this tool helps you explore options. You provide your problem's adjustable parameters and a way to score how good each set of parameters is. It then suggests the optimal configuration, telling you which settings yield the best results for your specific goal.
1,261 stars. Used by 1 other package. Actively maintained with 41 commits in the last 30 days. Available on PyPI.
Use this if you need to optimize a 'black-box' function or a system where you can't easily calculate gradients, such as tuning machine learning model hyperparameters or finding the best parameters for a complex simulation.
Not ideal if your optimization problem involves continuous, differentiable functions where gradient-based methods are efficient and feasible.
Stars
1,261
Forks
95
Language
Python
License
MIT
Category
Last pushed
Mar 07, 2026
Commits (30d)
41
Dependencies
3
Reverse dependents
1
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