qga-py 0.1.0

Quantum-inspired genetic and evolutionary algorithms for combinatorial optimization.

qga-py

Quantum-inspired genetic and evolutionary algorithms for combinatorial optimization on classical computers.

This project does not require quantum hardware. It implements practical quantum-inspired optimizers that maintain probability amplitudes or probability-like distributions, sample candidate solutions, evaluate them, and update the distributions toward better solutions.

Installation

For local development:

pip install -e ".[dev]"

After publishing:

pip install qga-py

Usage

Binary optimization:

from qga import BinaryQGA

def fitness(bits, _):
    return bits.sum()

optimizer = BinaryQGA(
    genome_length=100,
    population_size=20,
    generations=200,
    fitness_func=fitness,
)

result = optimizer.run()
print(result.best_solution, result.best_fitness)

Permutation optimization:

from qga import PermutationQGA

def fitness(route, distance):
    length = sum(distance[route[i - 1], route[i]] for i in range(len(route)))
    return -length

optimizer = PermutationQGA(
    n_items=20,
    population_size=40,
    generations=300,
    fitness_func=fitness,
    fitness_inputs=distance,
)

result = optimizer.run()
print(result.best_solution, result.best_fitness)

Included Examples

• examples/knapsack_demo.py
• examples/tsp_demo.py

Publishing Checklist

1. Update the project URLs in pyproject.toml.
2. Run tests with pytest.
3. Build with python -m build.
4. Upload to TestPyPI first with python -m twine upload --repository testpypi dist/*.
5. Install from TestPyPI and verify imports.
6. Upload to PyPI with python -m twine upload dist/*.

License

MIT