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DEAP provides a Python framework for prototyping evolutionary computation algorithms and testing new ideas.
About deap
DEAP, short for Distributed Evolutionary Algorithms in Python, is a framework for rapid prototyping and testing of evolutionary computation ideas. It seeks to make algorithms explicit and data structures transparent. The project provides a User's Guide and Jupyter notebook examples; the notebooks can be navigated and executed block by block.
Pricing is not clearly stated.
Key features
- Genetic algorithms with lists, arrays, sets, dictionaries, trees or NumPy arrays
- Genetic programming with loosely typed or strongly typed prefix trees and automatically defined functions
- Evolution strategies including CMA-ES
- Multi-objective optimisation with NSGA-II, NSGA-III, SPEA2 and MO-CMA-ES
- Co-evolution of multiple populations, cooperative or competitive
- Parallel evaluation, Hall of Fame tracking and periodic checkpoints
- Benchmark test functions and genealogy compatible with NetworkX
Best for: Prototyping and testing evolutionary computation ideas in Python.
Watch out for
- Using CMA-ES requires Numpy.
- Building the documentation from source requires Sphinx.
deap at a glance
- Website
- github.com/deap/deap
- Type
- Open-source project
- Pricing
- Not confirmed. Check the site
- Details verified
- Oct 22, 2026 · Official source
- Open source
- Yes · Source code
- Language
- English
- Last checked
- Oct 5, 2026 · Online when checked
- Listed
- Oct 5, 2026
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deap FAQ
How do I install DEAP?
The README recommends easy_install or pip and gives pip install deap as the command. It also warns that apt-get and yum usually provide an outdated version.
What license does DEAP use?
The repository lists an LGPL-3.0 license.
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