# geatpy-dev/geatpy

Evolutionary algorithm toolbox and framework with high performance for Python

Repository: https://github.com/geatpy-dev/geatpy
Canonical: https://ross.abutalabs.com/products/geatpy
Homepage: http://www.geatpy.com
Language: Python
License: LGPL-3.0
License Family: copyleft
Topics: geatpy, ga, de, es, nsga, rvea, moead, evolutionary-algorithms, high-performance, parallel-computing
Last push: 2026-02-05T13:54:05+00:00

## Health v2 (maintenance only)
Score: 52/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 66, release rhythm 8, longevity 100
- inputs: {"age_days": 2932, "days_push": 209, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2121, forks 727 (observed 2026-08-28T04:06:16.575105+00:00)

## What it is
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
- artifact type: library
- maturity: active
- function: machine-learning, simulation, math, benchmarking
- domain: artificial-intelligence, data-science, performance
- platform: python, windows, cross-platform
- tags: genetic-algorithms, evolutionary-computation, multi-objective-optimization, nsga2, differential-evolution, metaheuristics, optimization, algorithms, linux, macos

## Member repositories
- geatpy-dev/geatpy (main) score 52

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:16.575105+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T02:52:53.220125+00:00, confidence not recorded.
  - readme: https://github.com/geatpy-dev/geatpy (fetched 2026-08-28T04:06:16.575105+00:00, sha b177059654a0)
- Data as of 2026-08-30T08:39:29.467469+00:00.