geatpy-dev/geatpy

Health v2 · maintenance only

Adoption not part of the score

2122 stars · 728 forks observed · 2026-10-15

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Geatpy is a high-performance genetic and evolutionary algorithm toolbox for Python supporting single-objective, multi-objective, and many-objective optimization. It provides numerous evolutionary operators, algorithm templates (GA, DE, ES), chromosome encodings, parallel evaluation, and benchmark testbeds.

Use cases

• solve multi-objective optimization problems in python
• run genetic algorithm optimization
• use NSGA-II for many-objective optimization
• apply differential evolution to a fitness function
• parallelize fitness evaluations across a population
• benchmark evolutionary algorithms on standard test functions
• solve combinatorial optimization with evolutionary algorithms

When to choose

• you need fast, high-performance evolutionary operators in Python
• you need many built-in algorithm templates and chromosome encodings
• you need multi- or many-objective solvers like NSGA, RVEA, MOEA/D
• you want parallelized population evaluation

When to avoid

• you need gradient-based or convex optimization rather than metaheuristics
• you need a maintained library for Python > 3.10
• you want a pure-Python dependency-free solution (it requires numpy and matplotlib)
• you need hyperparameter tuning frameworks like Optuna instead of evolutionary search

Facets

library · maturity active

machine-learning simulation math benchmarking artificial-intelligence data-science performance python windows cross-platform genetic-algorithms evolutionary-computation multi-objective-optimization nsga2 differential-evolution metaheuristics optimization algorithms linux macos

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