galgopy
A lightweight Python package providing a basic but flexible implementation of the Genetic Algorithm (GA) for optimization tasks. It can be easily integrated into other projects or extended with custom selection, crossover, and mutation strategies.
Installation
galgopy is available on PyPI:
pip install galgopy
Usage
Sphere Function Optimization example
import numpy as np
from galgopy.crossover import IntermediateRecombination
from galgopy.ga import GA
from galgopy.mutation import UniformMutation
from galgopy.selection import TournamentSelection
ga = GA(
population_size=100,
chromosome_size=2,
gene_bounds=(-5.12, 5.12),
fitness_func=lambda x: -np.sum(np.square(x)),
mutation=UniformMutation(individual_rate=0.1, gene_bounds=(-5.12, 5.12)),
crossover=IntermediateRecombination(),
selection=TournamentSelection(),
)
# The course of 50 generations.
for _ in range(50):
next(ga)
# Result
best_chromosome, _ = ga.best_chromosome()
print(
f"Result:\t{best_chromosome}",
"Global minimum:\t[0. 0.]",
sep="\n",
)
More examples
More examples can be found in the examples directory.
- Booth Function Optimization
- One Max Problem
- String generation
- Travelling salesman problem (TSP)
License
This project is licensed under the MIT License.
1 maintainer
Metadata
1 maintainer
Release files for galgopy 0.1.1
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