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            "License :: OSI Approved :: Apache Software License",
            "Operating System :: OS Independent",
            "Programming Language :: Python",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7",
            "Programming Language :: Python :: 3.8",
            "Topic :: Scientific/Engineering",
            "Topic :: Scientific/Engineering :: Artificial Intelligence",
            "Topic :: Scientific/Engineering :: Mathematics"
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        "description": "\n.. |python| image:: https://img.shields.io/badge/python-3.9-blue.svg\n   :alt: python 3.9\n\n.. |license| image:: https://img.shields.io/badge/license-apache-orange.svg\n   :alt: license apache\n   :target: https://www.apache.org/licenses/LICENSE-2.0\n\n\n.. |logo| image:: https://raw.githubusercontent.com/anyoptimization/pymoo/master/data/logo.png\n  :target: https://pymoo.org\n  :alt: pymoo\n\n\n.. |animation| image:: https://raw.githubusercontent.com/anyoptimization/pymoo/master/data/animation.gif\n  :target: https://pymoo.org\n  :alt: pymoo\n\n\n.. _Github: https://github.com/anyoptimization/pymoo\n.. _Documentation: https://www.pymoo.org/\n.. _Paper: https://ieeexplore.ieee.org/document/9078759\n\n\n\n\n|python| |license|\n\n\n|logo|\n\n\n\nDocumentation_ / Paper_ / Installation_ / Usage_ / Citation_ / Contact_\n\n\n\npymoo: Multi-objective Optimization in Python\n====================================================================\n\nOur open-source framework pymoo offers state of the art single- and multi-objective algorithms and many more features\nrelated to multi-objective optimization such as visualization and decision making.\n\n\n.. _Installation:\n\nInstallation\n********************************************************************************\n\nFirst, make sure you have a Python 3 environment installed. We recommend miniconda3 or anaconda3.\n\nThe official release is always available at PyPi:\n\n.. code:: bash\n\n    pip install -U pymoo\n\n\nFor the current developer version:\n\n.. code:: bash\n\n    git clone https://github.com/anyoptimization/pymoo\n    cd pymoo\n    pip install .\n\n\nSince for speedup, some of the modules are also available compiled, you can double-check\nif the compilation worked. When executing the command, be sure not already being in the local pymoo\ndirectory because otherwise not the in site-packages installed version will be used.\n\n.. code:: bash\n\n    python -c \"from pymoo.util.function_loader import is_compiled;print('Compiled Extensions: ', is_compiled())\"\n\n\n.. _Usage:\n\nUsage\n********************************************************************************\n\nWe refer here to our documentation for all the details.\nHowever, for instance, executing NSGA2:\n\n.. code:: python\n\n\n    from pymoo.algorithms.moo.nsga2 import NSGA2\n    from pymoo.factory import get_problem\n    from pymoo.optimize import minimize\n    from pymoo.visualization.scatter import Scatter\n\n    problem = get_problem(\"zdt1\")\n\n    algorithm = NSGA2(pop_size=100)\n\n    res = minimize(problem,\n                   algorithm,\n                   ('n_gen', 200),\n                   seed=1,\n                   verbose=True)\n\n    plot = Scatter()\n    plot.add(problem.pareto_front(), plot_type=\"line\", color=\"black\", alpha=0.7)\n    plot.add(res.F, color=\"red\")\n    plot.show()\n\n\n\nA representative run of NSGA2 looks as follows:\n\n|animation|\n\n\n\n.. _Citation:\n\nCitation\n********************************************************************************\n\nIf you have used our framework for research purposes, you can cite our publication by:\n\n| `J. Blank and K. Deb, pymoo: Multi-Objective Optimization in Python, in IEEE Access, vol. 8, pp. 89497-89509, 2020, doi: 10.1109/ACCESS.2020.2990567 <https://ieeexplore.ieee.org/document/9078759>`_\n|\n| BibTex:\n\n::\n\n    @ARTICLE{pymoo,\n        author={J. {Blank} and K. {Deb}},\n        journal={IEEE Access},\n        title={pymoo: Multi-Objective Optimization in Python},\n        year={2020},\n        volume={8},\n        number={},\n        pages={89497-89509},\n    }\n\n.. _Contact:\n\nContact\n********************************************************************************\n\nFeel free to contact me if you have any questions:\n\n| `Julian Blank <http://julianblank.com>`_  (blankjul [at] egr.msu.edu)\n| Michigan State University\n| Computational Optimization and Innovation Laboratory (COIN)\n| East Lansing, MI 48824, USA\n\n\n\n\n\n",
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    "vulnerabilities": []
}