| Name | desdeo-emo JSON |
| Version |
1.4.2
JSON |
| download |
| home_page | |
| Summary | The python version reference vector guided evolutionary algorithm. |
| upload_time | 2022-12-07 19:50:08 |
| maintainer | |
| docs_url | None |
| author | Bhupinder Saini |
| requires_python | >=3.8.0,<3.10 |
| license | MPL-2.0 |
| keywords |
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| VCS |
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| bugtrack_url |
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| requirements |
No requirements were recorded.
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| Travis-CI |
No Travis.
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| coveralls test coverage |
No coveralls.
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# desdeo-emo
[](https://mybinder.org/v2/gh/industrial-optimization-group/desdeo-emo/master)
The evolutionary algorithms package within the [DESDEO framework](https://github.com/industrial-optimization-group/DESDEO).
Code for the SoftwareX paper can be found in [this notebook](docs/notebooks/Using_EvoNN_for_optimization.ipynb).
Currently supported:
* Multi-objective optimization with visualization and interaction support.
* Preference is accepted as a reference point.
* Surrogate modelling (neural networks and genetic trees) evolved via EAs.
* Surrogate assisted optimization
* Constraint handling using `RVEA`
* IOPIS optimization using `RVEA` and `NSGA-III`
Currently _NOT_ supported:
* Binary and integer variables.
To test the code, open the [binder link](https://mybinder.org/v2/gh/industrial-optimization-group/desdeo-emo/master) and read example.ipynb.
Read the documentation [here](https://desdeo-emo.readthedocs.io/en/latest/)
### Requirements
* Python 3.8 or newer.
* [Poetry dependency manager](https://github.com/sdispater/poetry): Only for developers
### Installation process for normal users
* Create a new virtual enviroment for the project
* Run: `pip install desdeo_emo`
### Installation process for developers
* Download and extract the code or `git clone`
* Create a new virtual environment for the project
* Run `poetry install` inside the virtual environment shell.
## Citation
If 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).
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"description": "# desdeo-emo\n[](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 3.8 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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