# DEAP/deap

Distributed Evolutionary Algorithms in Python

Repository: https://github.com/DEAP/deap
Canonical: https://ross.abutalabs.com/products/deap
Homepage: http://deap.readthedocs.org/
Language: Python
License: LGPL-3.0
License Family: copyleft
Last push: 2026-04-17T20:59:38+00:00

## Health v2 (maintenance only)
Score: 67/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 77, release rhythm 35, longevity 100
- inputs: {"age_days": 4487, "days_push": 138, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 6436, forks 1164 (observed 2026-08-28T04:09:43.399329+00:00)

## What it is
DEAP is a Python framework for evolutionary computation that supports genetic algorithms, genetic programming, evolution strategies, and multi-objective optimization. It emphasizes explicit algorithms and transparent data structures, and integrates with parallelization tools like multiprocessing and SCOOP.

## Use cases
- run a genetic algorithm to optimize parameters
- perform multi-objective optimization with NSGA-II
- do genetic programming with prefix trees
- implement CMA-ES evolution strategies
- parallelize fitness evaluations across processes
- evolve multiple cooperating or competing populations
- benchmark optimization test functions

## When to choose
- you need flexible evolutionary algorithms with custom representations in Python
- you want multi-objective optimizers like NSGA-II/III or SPEA2 out of the box
- you need to parallelize fitness evaluation easily
- you want rapid prototyping of novel evolutionary methods

## When to avoid
- you need gradient-based deep learning training
- you want a turnkey hyperparameter tuning tool with a high-level API
- your project requires a non-Python environment

## Facets
- artifact type: library
- maturity: stable
- function: machine-learning, simulation, benchmarking, concurrency
- domain: artificial-intelligence, machine-learning, performance
- platform: python, cross-platform
- tags: evolutionary-computation, genetic-algorithm, genetic-programming, multi-objective-optimization, cma-es, nsga-ii, particle-swarm-optimization, parallelization, algorithms

## Member repositories
- DEAP/deap (main) score 67

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:09:43.399329+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 2026-08-29T17:44:43.343918+00:00, confidence not recorded.
  - readme: https://github.com/DEAP/deap (fetched 2026-08-28T04:09:43.399329+00:00, sha 1c0738baf918)
- Data as of 2026-08-30T08:39:29.467469+00:00.