CMA-ES/pycma

Health v2 · maintenance only

Flags: no_license

Adoption not part of the score

1350 stars · 201 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

pycma is a Python implementation of the CMA-ES (Covariance Matrix Adaptation Evolution Strategy) derivative-free optimization algorithm, along with related numerical optimization tools. It handles difficult non-convex, ill-conditioned, multi-modal, noisy problems in continuous and mixed-integer search spaces, including bound, linear, and nonlinear constraints.

Use cases

• optimize a noisy black-box objective function without gradients
• solve non-convex multi-modal continuous optimization problems
• handle bound and nonlinear constraints in evolutionary optimization
• optimize functions with mixed-integer variables
• tune hyperparameters of a simulation or model
• use an ask-and-tell interface for interactive optimization loops

When to choose

• your objective function is non-convex, rugged, noisy, or ill-conditioned
• gradients are unavailable or expensive to compute
• you need constraint handling or mixed-integer support in continuous optimization
• you want a well-cited, reference implementation of CMA-ES in Python

When to avoid

• your problem is smooth and convex where gradient-based solvers are faster
• you need discrete/combinatorial optimization rather than continuous search spaces
• you need massively parallel large-scale optimization where CMA-ES covariance updates are too costly

Facets

library · maturity stable

math simulation machine-learning mathematics python cross-platform cma-es evolution-strategy derivative-free-optimization black-box-optimization numerical-optimization global-optimization algorithms

Member repositories

For agents

markdown · JSON · MCP: product_card(name="CMA-ES/pycma")