{"categories":[{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/2"},{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/3"},{"label":"Mathematics","url":"https://skillfed.io/packages/category/scientific-engineering-mathematics"}],"enrichment":{"capability":"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.","skillfed_tags":["optimization","evolutionary-algorithm","black-box"],"use_cases":["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"],"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.\n\nThe 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.","worth_installing":"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."},"id":"cma","links":{"html":"https://skillfed.io/packages/cma","md":"https://skillfed.io/packages/cma.md","pypi":"https://pypi.org/project/cma/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-02-25","license_spdx":"BSD-3-Clause","license_treatment":"permissive","name":"cma","python_support":"unspecified","summary":"CMA-ES, Covariance Matrix Adaptation Evolution Strategy for non-linear numerical optimization in Python"},"popularity":{"monthly_downloads":1813205,"position":3527,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"4.4.4"}