deap
Distributed Evolutionary Algorithms in Python
Decision gist · record as of 2026-08-14
Yes. DEAP is actively maintained, has low install friction, carries no known vulnerabilities, and is widely used in research and industry for evolutionary computation. The LGPL license is standard for research software and poses no barrier for most use cases. Install it if you need a mature, flexible framework for genetic algorithms, genetic programming, or multi-objective optimization.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Low install friction with a pure-Python wheel distribution.
- Actively maintained with a release within the last 119 days.
- Depends on numpy and moocore, both widely available.
License · maintenance · safety
LGPL (copyleft) — DEAP is licensed under LGPL (copyleft), which requires that derivative works and modifications remain under the same license, though linking from proprietary code is permitted under the library exception.
last release 2026-04-17 (119 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 500,364 downloads/mo, #6,321 on PyPI
Alternatives
Verify before relying
pip install deap
from deap import creator, base, tools, algorithms
import random
creator.create("FitnessMax", base.Fitness, weights=(1.0,))
creator.create("Individual", list, fitness=creator.FitnessMax)
toolbox = base.Toolbox()
toolbox.register("attr_bool", random.randint, 0, 1)
toolbox.register("individual", tools.initRepeat, creator.Individual, toolbox.attr_bool, n=100)
population = toolbox.population(n=300)- Whether moocore is a required runtime dependency or only needed for specific multi-objective optimization features
- Current Python version support range (requires_python not specified in metadata)
What it is and what it does
DEAP is a framework for building evolutionary algorithms in Python. It provides building blocks for genetic algorithms, genetic programming, evolution strategies (including CMA-ES), and multi-objective optimization (NSGA-II, NSGA-III, SPEA2, MO-CMA-ES). The framework emphasizes making algorithms explicit and data structures transparent, allowing you to work with any representation—lists, arrays, sets, dictionaries, trees, or numpy arrays. It integrates with numpy and moocore for numerical operations and multi-objective tasks.
The package is designed for rapid prototyping and testing of evolutionary ideas. It includes utilities like a Hall of Fame for tracking the best individuals, checkpoints for system snapshots, benchmarks with common test functions, and genealogy tracking compatible with NetworkX. DEAP supports parallelization of fitness evaluations through mechanisms like multiprocessing and SCOOP, making it suitable for computationally intensive optimization problems.
Use it for
- Implement a genetic algorithm to optimize parameters for a machine learning model or engineering design
- Solve multi-objective optimization problems where multiple competing objectives must be balanced
- Develop genetic programming solutions to evolve symbolic expressions or program trees for regression or classification
- Run evolution strategies like CMA-ES for continuous optimization of high-dimensional problems
- Prototype particle swarm optimization or differential evolution algorithms for comparison studies
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
DEAP is actively maintained, has low install friction, carries no known vulnerabilities, and is widely used in research and industry for evolutionary computation. The LGPL license is standard for research software and poses no barrier for most use cases. Install it if you need a mature, flexible framework for genetic algorithms, genetic programming, or multi-objective optimization.
Install
deap on PyPI
Before you install
Low install friction with a pure-Python wheel distribution. Actively maintained with a release within the last 119 days. Depends on numpy and moocore, both widely available.
License in practice
DEAP is licensed under LGPL (copyleft), which requires that derivative works and modifications remain under the same license, though linking from proprietary code is permitted under the library exception.
Quickstart
pip install deap
from deap import creator, base, tools, algorithms
import random
creator.create("FitnessMax", base.Fitness, weights=(1.0,))
creator.create("Individual", list, fitness=creator.FitnessMax)
toolbox = base.Toolbox()
toolbox.register("attr_bool", random.randint, 0, 1)
toolbox.register("individual", tools.initRepeat, creator.Individual, toolbox.attr_bool, n=100)
population = toolbox.population(n=300)Verify before relying
- Whether moocore is a required runtime dependency or only needed for specific multi-objective optimization features
- Current Python version support range (requires_python not specified in metadata)
Package facts
| License | LGPL copyleft |
| Python support | Not specified |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 2 packagesnumpymoocore |
| Maintenance | Actively maintained 119 days since the last release |
| First released | |
| Downloads | 500,364 / month, #6,321 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 4 - BetaIntended Audience :: DevelopersIntended Audience :: EducationIntended Audience :: Science/ResearchLicense :: OSI Approved :: GNU Library or Lesser General Public License (LGPL)Programming Language :: PythonProgramming Language :: Python :: 3Topic :: Scientific/EngineeringTopic :: Software Development |
Evidence: deap-1.4.4-py3-none-any.whl
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See also gepa · pygad · pymoo · pyswarms · cmaes · moocore · cma · ax-platform · numdifftools · paretoset