# Evomatic
[](https://evomatic.readthedocs.io/en/latest/?badge=latest)
Evomatic is an genetic algorithm tool for the development of new alloys.
## Installation
The evomatic package can be installed from
[pypi](https://pypi.org/project/evomatic/) using pip:
``pip install evomatic``
Evomatic makes heavy use of the
[metallurgy](https://github.com/Robert-Forrest/metallurgy) package to manipulate
and approximate properties of alloys. The
[cerebral](https://github.com/Robert-Forrest/cerebral) package can be used by
evomatic to obtain predictions of alloy properties on-the-fly during evolution.
## Usage
Basic usage of evomatic involves setting some targets and running an evolution,
the following example shows evolution of a small population towards alloys with
high mass:
```python
>>> import evomatic as evo
>>> evolver = evo.Evolver(targets={"maximise": ["mass"]}, population_size=50)
>>> history = evolver.evolve()
>>> history["alloys"]
alloy mass generation rank fitness
Og100 295.000000 7 0 1.000000
Og92.4Fl7.6 294.544000 11 1 0.998433
Og91.4Lv7.4Cm1.2 294.276000 9 2 0.997512
Og78Mc14Lv6.2Hs1.8 293.726000 11 3 0.995622
Lv100 293.000000 2 4 0.993127
... ... ... ... ...
Ar38.8Li28.5B24.9Sc5.7I2.1 25.396895 10 587 0.073521
Si79H21 22.398830 10 588 0.063218
Li83.7Zr16.3 20.678292 6 589 0.057305
Li97.9Db2.1 12.464260 5 590 0.029078
He100 4.002602 4 591 0.000000
```
In this simple example, there is no better material for the objective of maximum
mass than pure Oganesson.
## Documentation
Documentation is available [here.](https://evomatic.readthedocs.io/en/latest/api.html)
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"description": "# Evomatic\n\n\n[](https://evomatic.readthedocs.io/en/latest/?badge=latest)\n\nEvomatic is an genetic algorithm tool for the development of new alloys.\n\n## Installation\n\nThe evomatic package can be installed from\n[pypi](https://pypi.org/project/evomatic/) using pip:\n\n``pip install evomatic``\n\nEvomatic makes heavy use of the\n[metallurgy](https://github.com/Robert-Forrest/metallurgy) package to manipulate\nand approximate properties of alloys. The\n[cerebral](https://github.com/Robert-Forrest/cerebral) package can be used by\nevomatic to obtain predictions of alloy properties on-the-fly during evolution.\n\n## Usage\n\nBasic usage of evomatic involves setting some targets and running an evolution,\nthe following example shows evolution of a small population towards alloys with\nhigh mass:\n\n```python\n>>> import evomatic as evo\n\n>>> evolver = evo.Evolver(targets={\"maximise\": [\"mass\"]}, population_size=50)\n\n>>> history = evolver.evolve()\n\n>>> history[\"alloys\"]\n alloy mass generation rank fitness\n Og100 295.000000 7 0 1.000000\n Og92.4Fl7.6 294.544000 11 1 0.998433\n Og91.4Lv7.4Cm1.2 294.276000 9 2 0.997512\n Og78Mc14Lv6.2Hs1.8 293.726000 11 3 0.995622\n Lv100 293.000000 2 4 0.993127\n ... ... ... ... ...\n Ar38.8Li28.5B24.9Sc5.7I2.1 25.396895 10 587 0.073521\n Si79H21 22.398830 10 588 0.063218\n Li83.7Zr16.3 20.678292 6 589 0.057305\n Li97.9Db2.1 12.464260 5 590 0.029078\n He100 4.002602 4 591 0.000000\n```\n\nIn this simple example, there is no better material for the objective of maximum\nmass than pure Oganesson. \n\n## Documentation\n\nDocumentation is available [here.](https://evomatic.readthedocs.io/en/latest/api.html)\n\n",
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