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        "description": "# desdeo-emo\n[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/industrial-optimization-group/desdeo-emo/master)\n\nThe evolutionary algorithms package within the [DESDEO framework](https://github.com/industrial-optimization-group/DESDEO).\n\nCode for the SoftwareX paper can be found in [this notebook](docs/notebooks/Using_EvoNN_for_optimization.ipynb).\n\nCurrently supported:\n* Multi-objective optimization with visualization and interaction support.\n* Preference is accepted as a reference point.\n* Surrogate modelling (neural networks and genetic trees) evolved via EAs.\n* Surrogate assisted optimization\n* Constraint handling using `RVEA`\n* IOPIS optimization using `RVEA` and `NSGA-III`\n\nCurrently _NOT_ supported:\n* Binary and integer variables.\n\nTo test the code, open the [binder link](https://mybinder.org/v2/gh/industrial-optimization-group/desdeo-emo/master) and read example.ipynb.\n\nRead the documentation [here](https://desdeo-emo.readthedocs.io/en/latest/)\n\n### Requirements\n* Python 3.7 or newer.\n* [Poetry dependency manager](https://github.com/sdispater/poetry): Only for developers\n\n### Installation process for normal users\n* Create a new virtual enviroment for the project\n* Run: `pip install desdeo_emo`\n\n### Installation process for developers\n* Download and extract the code or `git clone`\n* Create a new virtual environment for the project\n* Run `poetry install` inside the virtual environment shell.\n\n## Citation\n\nIf you decide to use DESDEO is any of your works or research, we would appreciate you citing the appropiate paper published in [IEEE Access](https://doi.org/10.1109/ACCESS.2021.3123825) (open access).\n",
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