https://github.com/Project-Platypus/Platypus

A Free and Open Source Python Library for Multiobjective Optimization
https://github.com/Project-Platypus/Platypus

algorithm evolutionary genetic multiobjective optimization python

Last synced: about 3 hours ago
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A Free and Open Source Python Library for Multiobjective Optimization

• Host: GitHub
• URL: https://github.com/Project-Platypus/Platypus
• Owner: Project-Platypus
• License: gpl-3.0
• Created: 2015-10-09T19:19:17.000Z (about 10 years ago)
• Default Branch: master
• Last Pushed: 2025-10-25T02:41:11.000Z (23 days ago)
• Last Synced: 2025-11-10T05:19:23.566Z (7 days ago)
• Topics: algorithm, evolutionary, genetic, multiobjective, optimization, python
• Language: Python
• Size: 1.21 MB
• Stars: 629
• Watchers: 18
• Forks: 159
• Open Issues: 2
• Metadata Files:
  • Readme: README.md
  • Funding: .github/FUNDING.yml
  • License: COPYING

Awesome Lists containing this project

• awesome-python-data-science - Platypus - A Free and Open Source Python Library for Multiobjective Optimization. (Optimization / NLP)
• awesome-python-data-science - Platypus - A Free and Open Source Python Library for Multiobjective Optimization. (Optimization / Others)

README

          
# Platypus

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### What is Platypus?

Platypus is a framework for evolutionary computing in Python with a focus on

multiobjective evolutionary algorithms (MOEAs).  It differs from existing

optimization libraries, including PyGMO, Inspyred, DEAP, and Scipy, by providing

optimization algorithms and analysis tools for multiobjective optimization.

It currently supports NSGA-II, NSGA-III, MOEA/D, IBEA, Epsilon-MOEA, SPEA2, GDE3,

OMOPSO, SMPSO, and Epsilon-NSGA-II.  For more information, see our

[examples](examples/)

and [online documentation](http://platypus.readthedocs.org/en/latest/index.html).

### Example

For example, optimizing a simple biobjective problem with a single real-valued

decision variables is accomplished in Platypus with:

```python

    from platypus import NSGAII, Problem, Real

    def schaffer(x):

        return [x[0]**2, (x[0]-2)**2]

    problem = Problem(1, 2)

    problem.types[:] = Real(-10, 10)

    problem.function = schaffer

    algorithm = NSGAII(problem)

    algorithm.run(10000)

```

### Installation

To install the latest Platypus release, run the following command:

```

    pip install platypus-opt

```

To install the latest development version of Platypus, run the following commands:

```

    pip install -U build setuptools

    git clone https://github.com/Project-Platypus/Platypus.git

    cd Platypus

    python -m build

    python -m pip install --editable .

```

#### Anaconda

Platypus is also available via conda-forge.

```

    conda config --add channels conda-forge

    conda install platypus-opt

```

For more information, see the [feedstock](https://github.com/conda-forge/platypus-opt-feedstock).

### Citation

If you use this software in your work, please cite it as follows (APA style):

> Hadka, D. (2024). Platypus: A Framework for Evolutionary Computing in Python (Version 1.4.1) [Computer software].  Retrieved from https://github.com/Project-Platypus/Platypus.

### License

Platypus is released under the GNU General Public License.