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pymoo

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About pymoo

P

pymoo

Solves and validates single-, multi-, and many-objective optimization

pymoo is an agent skill for solving and validating single-, multi- and many-objective optimisation problems with the pymoo Python framework. It covers algorithms such as NSGA-II, NSGA-III, MOEA/D and SPEA2, benchmark problems such as ZDT and DTLZ, constraints, Pareto front approximations, customisable genetic operators and multi-criteria decision-making methods. It is most useful when objectives conflict and you want the trade-off solutions, which form a Pareto front. The skill targets stable pymoo 0.6.2, reviewed on 1 October 2026 against the current official documentation and native test runs. Installation is through uv, and the skill recommends pinning the version for reproducible environments. The released 0.6.2 wheel requires NumPy, SciPy, moocore, autograd, cma, matplotlib, alive_progress and Deprecated. NumPy 2 is supported. Joblib, Optuna and dill are separate dependencies for the recipes that use them. The skill notes that the released package metadata governs installation where the prose documentation differs. Use it when you are optimising designs or parameters against several goals and need to see the trade-offs.

View pymooAll AI Skills apps
Price
Free
Made by
k-dense-ai
Category
AI Skills
Website
github.com/k-dense-ai/scientific-agent-skills/pymoo