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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.problems 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 \u003chttps://ieeexplore.ieee.org/document/9078759\u003e`_\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 \u003chttp://julianblank.com\u003e`_  (blankjul [at] msu.edu)\n| Michigan State University\n| Computational Optimization and Innovation Laboratory (COIN)\n| East Lansing, MI 48824, USA\n\n\n\n","funding_links":[],"categories":["Optimization","Python"],"sub_categories":["Others"],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fanyoptimization%2Fpymoo","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fanyoptimization%2Fpymoo","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fanyoptimization%2Fpymoo/lists"}