finch-genetics 3.5.2

A flexible framework for evolutionary algorithms in Python

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

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.