---
id: cma
version: "4.4.4"
license: BSD-3-Clause
license_treatment: permissive
maintenance: active
---

# cma — CMA-ES, Covariance Matrix Adaptation Evolution Strategy for non-linear numerical optimization in Python

License: permissive · Maintenance: active · Downloads: 1.8M/mo

## What it is and what it does

CMA-ES (Covariance Matrix Adaptation Evolution Strategy) is a randomized, derivative-free optimization algorithm implemented in Python for solving difficult continuous and mixed-integer optimization problems. It is designed for objective functions where gradients are unavailable or unhelpful, the search space has dependent variables, and the landscape may be non-convex, ill-conditioned, multi-modal, or noisy. The package provides two independent implementations via the CMAEvolutionStrategy class and a basic purecma.CMAES variant.

The algorithm is most effective for problems with search space dimensions between five and a few hundred, requiring at least around 100 times the dimension in function evaluations. CMA-ES handles bound constraints, linear and nonlinear constraints, noise, and integer variables for mixed-integer problems. It offers both a direct optimization interface (fmin2) and an ask-and-tell interface for fine-grained control over the iteration loop. The package depends only on numpy for array operations.

Use it for:

- Optimize expensive black-box functions where gradients are unavailable or unreliable
- Solve non-convex or multi-modal optimization problems in 5-100+ dimensions
- Handle constrained optimization with bounds or nonlinear constraints
- Tune hyperparameters or design parameters in machine learning or engineering workflows
- Optimize noisy objective functions where derivative-based methods fail

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

CMA-ES is a derivative-free numerical optimization algorithm for difficult non-convex, multi-modal, or ill-conditioned optimization problems in continuous or mixed-integer search spaces.

Yes. CMA-ES is a well-maintained, production-stable algorithm with no known vulnerabilities, low install friction, and permissive licensing. Install it if you need derivative-free optimization for difficult non-convex problems in moderate dimensions; skip it if you have gradients available or are optimizing in very small dimensions where faster methods exist.

## Install

pip install cma
uv add cma
poetry add cma

## Installing cma

Before you install: Low friction install with only numpy as a runtime dependency. Active maintenance with recent releases and a stable codebase.

License in practice: BSD-3-Clause is a permissive license allowing commercial and private use with minimal restrictions.

Quickstart:

pip install cma

import cma
es = cma.CMAEvolutionStrategy(8 * [0], 0.5)
xopt, es = cma.fmin2(cma.ff.rosen, 8 * [0], 0.5)

Verify before relying:

- Whether the package supports Python 3.10+ or has specific version constraints beyond 'Python 3'

## Package facts

- License: BSD-3-Clause (permissive)
- Python support: unspecified
- Install friction: low
- Maintenance: active
- Downloads: 1.8M/month (top 5,000 on PyPI)
- Known vulnerabilities: none known

## Tags

derivative-free optimization, CMA-ES algorithm, non-convex optimization, black-box optimization, evolutionary strategy, continuous optimization, multi-modal optimization, optimization, evolutionary-algorithm, black-box

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