cma

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 on this page — 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

cma on PyPI

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

Evidence: cma-4.4.4-py3-none-any.whl

Keywords: optimization, CMA-ES, cmaes

Tags

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Further reading