fcmaes
A Python 3 gradient-free optimization library.
Choosing an optimization tool? Start with the
shared solver-selection and agent guide or the
optimization ecosystem overview.
Use fcmaes for Python black-box objectives, parallel retry, Pareto search
and quality diversity. Durable MCP jobs use the separate Rust workers;
this Python package does not include an MCP server.
- Documentation book
- Full README
- Tutorials
- Python API
- C++ API
- Examples API
- Native Rust implementation (with PyO3 API for Python users)
- Optimization jobs: durable jobs and an MCP server for AI agents, with fcmaes-rust workers
- AI agent context: how an AI agent chooses, configures and validates fcmaes optimizers (AGENTS.md for coding agents)
Installation
python -m pip install fcmaes
To build from source you need a C++17 compiler and Python 3.10 or newer;
pip fetches the build tools, and Eigen is fetched if it is not installed:
git clone https://github.com/dietmarwo/fast-cma-es
cd fast-cma-es
python -m pip install . --config-settings=cmake.define.FCMAES_FETCH_EIGEN=ON
See the full README for the requirements, the changelog for what changed, and RELEASING.md for the release process.
Exact 2D Pareto archive
fcmaes.pareto.ParetoArchive2D keeps all nondominated evaluations of a
two-objective problem and updates their hypervolume with every insertion.
Its native code is derived from bitehv2.h in Aleksey Vaneev's
BiteOpt repository. See
usage and a MODE example.
Third-party integration
OptunaHub's FastCmaesSampler, by Jacob Pfeil, connects fcmaes CMA-ES to Optuna. See its example and supported Optuna version there. This is an external integration, not a comparative benchmark of the five-project ecosystem.
1 maintainer
Metadata
1 maintainer
Release files for fcmaes 2.1.0
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Source distribution (sdist)
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|---|---|---|---|---|
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Built distributions (wheels)
Total release size: 39.0 MB
Release files / fcmaes-2.1.0.tar.gz
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