https://github.com/geneticpy/geneticpy

GeneticPy is an optimizer that uses a genetic algorithm to quickly search through custom parameter spaces for optimal solutions.
https://github.com/geneticpy/geneticpy

genetic-algorithm hyperparameter-optimization optimization python

Last synced: 6 months ago
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GeneticPy is an optimizer that uses a genetic algorithm to quickly search through custom parameter spaces for optimal solutions.

• Host: GitHub
• URL: https://github.com/geneticpy/geneticpy
• Owner: geneticpy
• License: mit
• Created: 2018-12-02T21:25:20.000Z (over 7 years ago)
• Default Branch: main
• Last Pushed: 2024-09-03T23:56:45.000Z (almost 2 years ago)
• Last Synced: 2025-09-15T02:26:51.001Z (10 months ago)
• Topics: genetic-algorithm, hyperparameter-optimization, optimization, python
• Language: Python
• Homepage:
• Size: 75.2 KB
• Stars: 10
• Watchers: 1
• Forks: 1
• Open Issues: 5
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# GeneticPy

[![codecov](https://codecov.io/gh/geneticpy/geneticpy/branch/master/graph/badge.svg)](https://codecov.io/gh/geneticpy/geneticpy)

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GeneticPy is an optimizer that uses a genetic algorithm to quickly search through custom parameter spaces for optimal solutions.

### Installation

GeneticPy requires Python 3.10+

```sh

pip install geneticpy

```

### Development Workflow

This project uses [uv](https://github.com/astral-sh/uv) for fast dependency management and [hatchling](https://github.com/pypa/hatch) as the build backend.

```sh

# Run tests

make test

# Build the package

make build

```

### Optimize Example:

A brief example to get you started is included below:

```python

import geneticpy

def loss_function(params):

  if params['type'] == 'add':

    return params['x'] + params['y']

  elif params['type'] == 'multiply':

    return params['x'] * params['y']

param_space = {'type': geneticpy.ChoiceDistribution(choice_list=['add', 'multiply']),

               'x': geneticpy.UniformDistribution(low=5, high=10, q=1),

               'y': geneticpy.GaussianDistribution(mean=0, standard_deviation=1)}

results = geneticpy.optimize(loss_function, param_space, size=200, generation_count=500, verbose=True)

best_params = results.best_params

loss = results.best_score

total_time = results.total_time

```

### PyPi Project

https://pypi.org/project/geneticpy/

### Contact

Please feel free to email me at brandonschabell@gmail.com with any questions or feedback.