Training & EvalMachine Learning & Research AIK-Dense-AI/claude-scientific-skillsModel Training & Evaluation

PyMOO

Maintainer K-Dense Inc. · Last updated April 1, 2026

Pymoo is a comprehensive Python framework for optimi.

Claude CodeOpenClawNanoClawTrainingEvaluationpymoomachine-learningpackagemachine learning & deep learning

Original source

K-Dense-AI/claude-scientific-skills

https://github.com/K-Dense-AI/claude-scientific-skills/tree/main/scientific-skills/pymoo

Maintainer: K-Dense Inc.
License: Apache-2.0 license
Last updated: April 1, 2026

Skill Snapshot

Key Details From SKILL.md

2 min

Key Notes

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.
problem = get_problem("rastrigin", n_var=10).

Source Doc

Excerpt From SKILL.md

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

The Unified Interface

Pymoo uses a consistent minimize() function for all optimization tasks:

Result object contains:

result.X: Decision variables of optimal solution(s)
result.F: Objective values of optimal solution(s)
result.G: Constraint violations (if constrained)
result.algorithm: Algorithm object with history

Problem Types

Single-objective: One objective to minimize/maximize Multi-objective: 2-3 conflicting objectives → Pareto front Many-objective: 4+ objectives → High-dimensional Pareto front Constrained: Objectives + inequality/equality constraints Dynamic: Time-varying objectives or constraints