{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/3"}],"enrichment":{"capability":"Provides CMA-ES (Covariance Matrix Adaptation Evolution Strategy) optimization in an ask-and-tell interface, supporting continuous, integer, and categorical variable optimization with variants for mixed-variable and multi-objective problems.","skillfed_tags":["optimization","evolutionary-algorithms","hyperparameter-tuning"],"use_cases":["Hyperparameter tuning for machine learning models when gradient-based methods are impractical or unavailable.","Optimizing expensive simulations or physical experiments where function evaluations are costly and noisy.","Mixed-variable optimization combining continuous parameters, discrete choices, and categorical selections in a single problem.","Multi-objective optimization balancing competing objectives in mixed-variable spaces.","Black-box function optimization where the objective function is a complex pipeline or external tool."],"what_it_does":"cmaes is a Python implementation of the Covariance Matrix Adaptation Evolution Strategy, a derivative-free optimization algorithm designed to minimize or maximize black-box objective functions. It uses an ask-and-tell interface where you request candidate solutions from the optimizer, evaluate them with your objective function, and report results back\u2014making it suitable for expensive or noisy function evaluations where gradients are unavailable or unreliable.\n\nThe library supports multiple problem types: standard continuous optimization via the CMA class, mixed continuous-integer-categorical optimization via CatCMAwM, and multi-objective mixed-variable problems via COMOCatCMAwM. It depends only on numpy and integrates with Optuna for hyperparameter tuning workflows. The implementation is actively maintained and supports Python 3.9 through 3.14.","worth_installing":"Yes. Low install friction (numpy only), active maintenance, MIT license, no known vulnerabilities, and a focused implementation of a well-established algorithm make it a solid choice for derivative-free optimization. Install if you need CMA-ES specifically; if you're unsure whether CMA-ES is the right algorithm for your problem, verify that first."},"id":"cmaes","links":{"html":"https://skillfed.io/packages/cmaes","md":"https://skillfed.io/packages/cmaes.md","pypi":"https://pypi.org/project/cmaes/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-03-28","license_spdx":"MIT","license_treatment":"permissive","name":"cmaes","python_support":"supports_current","summary":"Lightweight Covariance Matrix Adaptation Evolution Strategy (CMA-ES) implementation for Python 3."},"popularity":{"monthly_downloads":1335688,"position":4034,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"0.13.0"}