If you're optimizing anything with multiple conflicting objectives, this is the framework you want. It implements the heavy hitters like NSGA-II and NSGA-III for finding Pareto fronts, handles constrained problems cleanly, and includes benchmark suites like ZDT and DTLZ for testing. The interface is consistent across algorithms, which matters when you're experimenting. It scales from simple single-objective GAs to many-objective problems with 5+ objectives. The constraint handling is thoughtful, defaulting to feasibility-first but giving you penalty methods when needed. Honestly, the main value is that someone already implemented these algorithms correctly so you don't have to debug your own NSGA-II implementation at 2am.
- skills.sh installs
- 300
- GitHub stars
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- last updated
- Aug 21, 2026
Install
$ npx -y skills add davila7/claude-code-templates --skill pymoo --agent claude-codeInstalls into .claude/skills of the current project.
Files
Pymoo - Multi-Objective Optimization in Python
Overview
Pymoo is a comprehensive Python framework for optimization with emphasis on multi-objective problems. Solve single and multi-objective optimization using state-of-the-art algorithms (NSGA-II/III, MOEA/D), benchmark problems (ZDT, DTLZ), customizable genetic operators, and multi-criteria decision making methods. Excels at finding trade-off solutions (Pareto fronts) for problems with conflicting objectives.
When to Use This Skill
This skill should be used when:
- Solving optimization problems with one or multiple objectives
- Finding Pareto-optimal solutions and analyzing trade-offs
- Implementing evolutionary algorithms (GA, DE, PSO, NSGA-II/III)
- Working with constrained optimization problems
- Benchmarking algorithms on standard test problems (ZDT, DTLZ, WFG)
- Customizing genetic operators (crossover, mutation, selection)
- Visualizing high-dimensional optimization results
- Making decisions from multiple competing solutions
- Handling binary, discrete, continuous, or mixed-variable problems
Core Concepts
The Unified Interface
Pymoo uses a consistent minimize() function for all optimization tasks:
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- Repository
- github.com/davila7/claude-code-templates
- Category
- Design & UI/UX
- First seen
- Jun 3, 2026
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