pygena

A simple, flexible genetic algorithm library for Python. Easily define your own chromosomes, fitness functions, and evolutionary strategies to solve optimization and search problems.

Features

• Minimal, intuitive API
• Customizable chromosomes and fitness functions
• Population management and evolution

Installation

pip install pygena

Or clone and install from source:

git clone https://github.com/atasoglu/pygena.git
cd pygena
pip install .

Usage

Here's a minimal example of evolving a list of numbers to sum to a target:

from pygena import Chromosome, Population
import random

target = 100
chromosome_size = 10
population_size = 10
mutation_rate = 0.05
iterations = 100

def random_int(gene=None):
    return random.randint(-100, 100)

def random_list():
    return [random_int() for _ in range(chromosome_size)]

def fitness_fn(chromosome):
    diff = abs(sum(chromosome.genes) - target)
    return 1 / (diff + 1e-5)

random.seed(42)
population = Population(
    chromosomes=[Chromosome(random_list()) for _ in range(population_size)],
    mutation_rate=mutation_rate,
    mutation_fn=random_int,
)
for i, local_best, global_best in population.run(iterations, fitness_fn):
    print(f"Iteration {i}: {global_best.genes} (sum: {sum(global_best.genes)})")
    if sum(global_best.genes) == target:
        print(f"Target reached in {i} iterations.")
        break

For more examples, see the examples/ directory.

You can simply run an example script: python3 -m pygena.examples.text. Run with the --help flag to see the full list of arguments.

Contributing

Contributions are welcome! Feel free to open issues or submit pull requests to help improve pygena.

License

MIT License. See LICENSE.