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https://github.com/dadukhankevin/finch
🐤The next evolution of evolution.
https://github.com/dadukhankevin/finch
artificial-intelligence evolutionary-algorithms genetic-algorithm genetic-algorithms keras python3
Last synced: 7 days ago
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🐤The next evolution of evolution.
- Host: GitHub
- URL: https://github.com/dadukhankevin/finch
- Owner: dadukhankevin
- Created: 2022-10-27T19:29:56.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2024-10-02T02:13:14.000Z (4 months ago)
- Last Synced: 2025-01-23T04:08:48.181Z (7 days ago)
- Topics: artificial-intelligence, evolutionary-algorithms, genetic-algorithm, genetic-algorithms, keras, python3
- Language: Python
- Homepage:
- Size: 1.21 MB
- Stars: 38
- Watchers: 4
- Forks: 3
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Finch: Evolutionary Algorithm Framework
Finch is a Python framework for implementing evolutionary algorithms. It provides a modular approach to building and experimenting with various evolutionary computation techniques.
## Key Features
- Modular design with customizable components
- Support for different types of genes (float arrays, strings, arrays)
- Various selection, crossover, and mutation operators
- GPU acceleration support using CuPy
- Visualization tools for monitoring evolution progress
## Main Components
1. **GenePool**: Generates initial populations
- FloatPool, StringPool, ArrayPool, ImagePool
2. **Individual**: Represents a single solution in the population
3. **Layer**: Defines genetic operators
- Selection layers
- Crossover layers (e.g., N-Point, Uniform)
- Mutation layers (e.g., Gaussian, Uniform, Polynomial, Swap, Inversion, Scramble)
4. **Environment**: Manages the evolution process
5. **Competition**: Allows comparing multiple evolutionary strategies
## Usage
1. Define your fitness function
2. Create a GenePool
3. Set up Layers for selection, crossover, and mutation
4. Initialize an Environment with your layers and individuals
5. Run the evolution process
## Example
```python
import Finch.layers as layers
from Finch.selectors import *
from Finch.generic import *
def fitness_function(individual):
return sum(individual.item)
gene_pool = layers.float_arrays.FloatPool(ranges=[[-5, 5]] * 10, length=10, fitness_function=fitness_function)
mutation_selection = RandomSelection(percent_to_select=.1)
crossover_selection = RandomSelection(amount_to_select=2)
# Set up layers
layers = [
layers.universal_layers.Populate(population=500, gene_pool=gene_pool),
layers.array_layers.ParentNPoint(selection_function=crossover_selection.select, families=4, children=4),
layers.float_arrays.GaussianMutation(mutation_rate=0.1, sigma=0.5, selection_function=mutation_selection.select),
layers.universal_layers.SortByFitness(),
layers.universal_layers.CapPopulation(1000),
]
env = Environment(layers)
env.compile()
env.evolve(generations=1000)
print(env.best_ever.item)
env.plot()
```
## Installation
```
pip install finch-genetics
```
## Contributing
Contributions are welcome! Please feel free to submit a Pull Request.
## License
This project is licensed under the MIT License.