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GeneticAlgos-1.0.2
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مشاهده بیشترتوضیحات
Simple and powerful Python library for creating genetic algorithms.
| ویژگی | مقدار |
|---|---|
| سیستم عامل | OS Independent |
| نام فایل | GeneticAlgos-1.0.2 |
| نام | GeneticAlgos |
| نسخه کتابخانه | 1.0.2 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Lukas Kozelnicky |
| ایمیل نویسنده | python@kozelnicky.com |
| آدرس صفحه اصلی | https://geneticalgos.readthedocs.io/en/latest/ |
| آدرس اینترنتی | https://pypi.org/project/GeneticAlgos/ |
| مجوز | MIT |
GeneticAlgos
============
.. image:: https://github.com/lkozelnicky/GeneticAlgos/workflows/Tests/badge.svg
:target: https://github.com/lkozelnicky/GeneticAlgos/actions?query=workflow%3ATests+branch%3Amaster
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:target: https://coveralls.io/github/lkozelnicky/GeneticAlgos?branch=master
.. image:: https://img.shields.io/badge/code%20style-black-000000.svg
:target: https://github.com/psf/black
.. image:: https://img.shields.io/pypi/v/GeneticAlgos.svg
:target: https://pypi.python.org/pypi/GeneticAlgos
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.. image:: https://raw.githubusercontent.com/lkozelnicky/GeneticAlgos/master/docs/_static/GeneticAlgos.png
:align: center
:alt: GenticAlgos logo
GeneticAlgos is a simple and powerful Python library for creating genetic algorithms to solve
complex optimization problems. GeneticAlgos is built on NumPy and it is under active development.
- Uses smart defaults for genetic algorithms parameters which are good enough for generic use cases or for testing.
- A simple-to-use API to modify genetic algorithms parameters.
- Lightweight and just one dependency - Numpy.
- Excellent test coverage.
- Tested on Python 3.7, 3.8, 3.9 and 3.10
Documentation
_____________
Online documentation is available at `https://geneticalgos.readthedocs.io/en/latest/ <https://geneticalgos.readthedocs.io/en/latest/>`_.
The docs include a `introduction to genetic algorithms <https://geneticalgos.readthedocs.io/en/latest/introduction.html>`_,
`examples <https://geneticalgos.readthedocs.io/en/latest/examples.html>`_, `advanced usage <https://geneticalgos.readthedocs.io/en/latest/advanced.html>`_,
and other useful information.
Usage
_____
``geneticalgos`` is available on `PYPI <https://pypi.python.org/pypi/GeneticAlgos/>`_.
Install with ``pip``:
.. code-block:: bash
$ pip install geneticalgos
**Trivial example**:
We want to find a set of ``X=(x1, x2, x3, x4)`` which maximizes sum(x1, x2, x3, x4),
when each element x is a float from interval (0, 10). Simple answer is: ``x1 = 10, x2 = 10, x3 = 10, x4 = 10``.
First, we define our fitness function (sum) and then gene_intervals for each x.
All other parameters (population size, crossover method, mutation probability, ...) are configured
with default values. However, you can change and tweak them easily - `Advanced usage <https://geneticalgos.readthedocs.io/en/latest/advanced.html>`__.
.. code-block:: python
import geneticalgos as ga
import numpy as np
def custom_fitness_function(chromosome):
return sum(chromosome)
gene_intervals = np.array([[0, 10]] * 4)
# Create genetic algorithms with default values for GA parameters
# and our fitness function and gene intervals
ga_model = ga.GeneticAlgo(
fitness_function=custom_fitness_function,
gene_intervals=gene_intervals,
)
# Start genetic algorithm simulation
ga_model.simulate()
# print best solution
print(ga_model.best_chromosome)
# print fitness value for best chromosome
print(ga_model.best_fitness)
When to use GeneticAlgos
________________________
The main goal of the GeneticAlgos is to be `simple` and `powerful`.
* Simple, because it can be used with **basic knowledge of python** (data structures, functions, ...).
* Simple, because it can be used with **basic knowledge of genetic algorithms** (population, chromosome, fitness function, ...).
* Powerful, because **we can tweak many genetic algorithms parameters** very easily and create complex models with the minimum of configuration.
When **not** to use GeneticAlgos
________________________________
Let's be honest, genetic algorithms are very complex algorithms which have a lot of modifications
from a standard scheme.
You should look somewhere else if you need:
* Something other than binary or numerical encoding - like permutation, strings, ...
* Chromosome genes with different encoding within same chromosome - some genes are float numbers and some of them integers.
* An end criterion that is different from a fixed number of generation cycles.
Issues
______
If you encounter any problems, please `file an issue <http://github.com/lkozelnicky/GeneticAlgos/issues>`_
along with a detailed description. Thank you 😃
About GeneticAlgos
__________________
Created by `Lukas Kozelnicky`.
Distributed under the MIT license. See ``LICENSE.txt`` for more information.
نیازمندی
| مقدار | نام |
|---|---|
| - | numpy |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.7 | Python |
نحوه نصب
نصب پکیج whl GeneticAlgos-1.0.2:
pip install GeneticAlgos-1.0.2.whl
نصب پکیج tar.gz GeneticAlgos-1.0.2:
pip install GeneticAlgos-1.0.2.tar.gz