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    "info": {
        "author": "Dmitriy Bobir",
        "author_email": "bobirdima@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Intended Audience :: Developers",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: Apache Software License",
            "License :: Public Domain",
            "Operating System :: Microsoft :: Windows",
            "Operating System :: POSIX :: Linux",
            "Programming Language :: Python",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.5",
            "Topic :: Scientific/Engineering",
            "Topic :: Software Development :: Libraries",
            "Topic :: Software Development :: Libraries :: Python Modules"
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        "description": "geneticalgs\n===========\n\nImplementation of standard, migration and diffusion models of genetic algorithms (GA) in ``python 3.5``.\n\nBenchmarking was conducted by `COCO platform <http://coco.gforge.inria.fr/>`__ ``v15.03``.\n\nThe project summary may be found in ``project_summary.pdf``.\n\nLink to `GitHub <https://github.com/bobirdmi/genetic-algorithms>`__\n\nLink to `PyPI <https://pypi.python.org/pypi/geneticalgs>`__.\n\nLink to `Read The Docs <https://readthedocs.org/projects/genetic-algorithms/>`__.\n\nImplemented features\n====================\n\n- standard, diffusion and migration models\n\n  - with real values (searching for global minimum or maximum of the specified function)\n\n  - with binary encoding combination of some input data\n\n- old population is completely replaced with a new computed one at the end of each generation (generational population model)\n\n- two types of fitness value optimalization\n\n  - minimization\n\n  - maximization\n\n- three parent selection types\n\n  - *roulette wheel selection*\n\n  - *rank wheel selection*\n\n  - *tournament*\n\n- may be specified mutation probability\n\n- may be specified any amount of random bits to be mutated\n\n- may be specified crossover probability\n\n- different types of crossover\n\n  - single-point\n\n  - two-point\n\n  - multiple point up to uniform crossover\n\n- elitism may be turned on/off (the best individual may migrate to the next generation)\n\nContent description\n===================\n\n- **/geneticalgs/** contains source codes\n\n- **/docs/** contains `sphinx <http://www.sphinx-doc.org/en/stable/>`__ source codes\n\n- **/2.7/** contains files converted from ``python 3.5`` to ``python 2.7`` using `3to2 module <https://pypi.python.org/pypi/3to2>`__ as `COCO platform <http://coco.gforge.inria.fr/>`__ used in benchmarking supports only this version of python. These files (not installed package ``geneticalgs``) are used in benchmarking. Must be copied in the directory with ``my_experiment.py`` or ``my_timing.py``.\n\n- **/2.7/benchmark/** contains the following files:\n\n  - ``my_experiment.py`` is used for running benchmarking. Read more `here <http://coco.lri.fr/COCOdoc/runningexp.html#python>`__.\n\n  - ``my_timing.py`` is used for time complexity measurements. It has the same run conditions as the previous file.\n\n  - ``pproc.py`` is a modified file from COCO platform distribution that must be copied to ``bbob.v15.03/python/bbob_pproc/`` in order to post-process measured data of migration GA (other models don't need it). It is necessary due to unexpected format of records in case of migration GA.\n\n- **/benchmarking/** contains measured results and the appropriate plots of benchmarking.\n\n- **/time_complexity/** contains time results measured using ``my_timing.py``.\n\n- **/examples/** contains examples of using the implemented genetic algorithms.\n\n- **/tests/** contains `pytest <http://doc.pytest.org/en/latest/>`__ tests\n\nRequirements\n============\n\n- python 3.5+\n\n- `NumPy <http://www.numpy.org/>`__\n\n- `bitstring <https://pypi.python.org/pypi/bitstring/>`__\n\n- `sphinx <http://www.sphinx-doc.org/en/stable/>`__ for documentation\n\n- `pytest <http://doc.pytest.org/en/latest/>`__ for tests\n\nInstallation\n============\n\nInstall package by typing the command\n\n``python -m pip install geneticalgs``\n\nIf you have problems installing NumPy it is **strongly** recommended to use `Anaconda <https://docs.continuum.io/>`__.\n\nRunning tests\n=============\n\nYou may run tests by typing from the package directory\n\n``python setup.py test``\n\nDocumentation\n=============\n\nGo to the package directory and then to ``docs/`` and type\n\n``pip install -r requirements.txt``\n\nThen type the following command in order to generate documentation in HTML\n\n``make html``\n\nAnd run doctest\n\n``make doctest``\n\nLicense\n=======\n\nLicensed under `Apache License Version 2.0 <http://www.apache.org/licenses/LICENSE-2.0>`__.",
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        "keywords": "evolutionary algorithms,genetic algorithms,optimalization,best combination,function minimum,function maximum",
        "license": "Public Domain",
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        "name": "geneticalgs",
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