https://github.com/ngocbh/geneticpython
A simple and user-friendly Python framework for genetic-based algorithms
https://github.com/ngocbh/geneticpython
algorithms evolutionary-algorithms genetic-algorithm multiobjective-optimization nsga-ii python-framework
Last synced: about 1 month ago
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A simple and user-friendly Python framework for genetic-based algorithms
- Host: GitHub
- URL: https://github.com/ngocbh/geneticpython
- Owner: ngocbh
- License: mit
- Created: 2020-04-12T03:27:28.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2023-02-22T16:54:59.000Z (over 2 years ago)
- Last Synced: 2025-08-20T22:52:27.544Z (about 2 months ago)
- Topics: algorithms, evolutionary-algorithms, genetic-algorithm, multiobjective-optimization, nsga-ii, python-framework
- Language: Python
- Homepage:
- Size: 153 MB
- Stars: 4
- Watchers: 1
- Forks: 1
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
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README
# geneticpython
A simple and friendly Python framework for genetic-based algorithms (strongly supports tree-encoding)
* Supported algorithms: Genetic Algorithm (`GAEngine`), NSGA-ii (`NSGAIIEngine`).
* An [example](https://github.com/ngocjr7/geneticpython/tree/master/examples) on ZDT1 problem:
## Installation
This package requires `python 3.6` or later.
```
pip install geneticpython
```
## Getting started
We can quickly design a genetic algorithm in the following steps:
1. define a individual template with specific encoding
```python
from geneticpython.models import BinaryIndividual
indv_temp = BinaryIndividual(length=100)
```
2. define population based on created individual template. This population can uniformly initialize a population or you can define your own by passing `init_population` argument function
```python
from geneticpython import Population
population = Population(indv_temp, pop_size=100)
```
3. define some core operators in genetic algorithm
```python
from geneticpython.core.operators import RouletteWheelSelection, UniformCrossover, \
FlipBitMutation, RouletteWheelReplacement
selection = RouletteWheelSelection()
crossover = UniformCrossover(pc=0.8, pe=0.5)
mutation = FlipBitMutation(pm=0.1)
# this function decides which individuals will be survived
replacement = RouletteWheelReplacement()
```
4. create an engine and register the defined population and operators
```python
from geneticpython import GAEngine
engine = GAEngine(population, selection=selection,
selection_size=100,
crossover=crossover,
mutation=mutation,
replacement=replacement)
```
5. register fitness function which gets an individual and returns its fitness value
```python
@engine.maximize_objective
def fitness(indv):
return fitness_of_indv
```
6. run engine
```python
engine.create_seed(seed)
history = engine.run(generations=1000)
```
7. get results and plot history
```python
ans = engine.get_best_indv()
print(ans)
plot_single_objective_history({'geneticpython': history})
```
You can find more examples [here](https://github.com/ngocjr7/geneticpython/tree/master/examples)
## Issues
This project is in development, if you find any issues, please create an issue [here](https://github.com/ngocjr7/geneticpython/issues).
## TODO
* [ ] Create extensive documentation and docs and comments in source-code
* [ ] Implement other algorithms: `PSO, DE, MOED/A, MOPSO, MODE,...`
* [ ] Implement other operators: `PMX crossover, ...`
* [ ] Create unit tests.
## Contributing
The goal of this project is to be able to build a simple and novice-friendly library yet functional enough to experiment with research projects. It is spontaneous and non-profit and also flawed.
We appreciate all contributions. If you are interested in contributing this project (including functional implementation or standard examples), please check [Contribution page](https://github.com/ngocjr7/geneticpython/blob/master/CONTRIBUTING.md).
If you plan to contribute new features, utility functions, fix bugs, or extensions to the core, please first open an issue and discuss the feature with us.
## Contributors
Ngoc Bui ([ngocjr7](https://github.com/ngocjr7))
## Acknowledgements
Special thanks to https://github.com/PytLab/gaft for getting me started a great API design.
This repository includes some parts of the following repos:
* https://github.com/msu-coinlab/pymoo
* https://github.com/tensorflow/tensorflow