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    "info": {
        "author": "WJ Maj",
        "author_email": "wjmaj98@gmail.com",
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        "classifiers": [
            "Development Status :: 4 - Beta",
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            "Programming Language :: Python",
            "Programming Language :: Python :: 3",
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        "description": "# Genetic Optimizer\n_Genetic algorithm optimizer using K-Means clustering with one way ANOVA algorithms_\n\n[![Build Status](https://travis-ci.org/szachovy/GeneticOptimizer.svg?branch=master)](https://travis-ci.org/szachovy/GeneticOptimizer)\n[![PyPI version](https://badge.fury.io/py/genetic-optimizer.svg)](https://badge.fury.io/py/genetic-optimizer)\n\n## Before:\n![github-small](https://github.com/szachovy/GeneticOptimizer/blob/master/Images/first.png)\n\n## After:\n![github-small](https://github.com/szachovy/GeneticOptimizer/blob/master/Images/last.svg)\n\n## Installation:\n\nUse the package manager [pip](https://pip.pypa.io/en/stable/) to install genetic_optimizer.\n\n```bash\npip3 install genetic-optimizer\n```\n\n### Note:\n\nPackage require =>Python 3.6.5, using genetic_optimizer in Python 2.x.x projects may not work correctly.\nIf you are not sure about your python version, try:\n\n```bash\npython3 --version\n```\n\n## Usage\n\n```python\nimport genetic_optimizer\n\nsome_object = genetic_optimizer.Optimizer()\nsome_object.generate(population_size=46, chromosome_size=8, equal_chromosomes=True, initialization_method='Random', representation='Binary', saving_method='csv')\n```\n\n_These are default options for generator, you can change them in DEFAULTS.ini file in package directory_\n\n```python\nsome_object.optimize(data=None, iterations=12, shuffle_scale=0.6, variety=0.8, chromosome_weight=0.0000001)\n```\n\n_These are default options for optimizer, you can change them in STANDARDS.conf file in package directory_\n\n### Note\n\n- If optimizer got stuck at the beginning, that usually means that your dataframe with population is to small to find appropiate parents in order to create next generations.\n\n- For more description, please check out project wiki.\n\n## Pros\n- There is no mutation and crossover probability. Program matches parents according to group differences and create new child, built from the _most successful_ pairs of genes from them.\n  That means new generations are closer to each one and every child is _not worse_ that _genetic worse_ parent.\n\n- You can play off with options to achive more precised results.\n\n## Cons\n\n- Fitness and parent selection process takes place recursively. If you are not using some RDD computing, program execution may be longer.\n\n## Contributing\n\nPull requests are welcome. \nFor major changes, please open an issue first to discuss what you would like to change.\n\n## License\n\n[MIT](https://choosealicense.com/licenses/mit/)\n\n## Tools\n\n- [micro editor](https://micro-editor.github.io/)\n- Unix console\n\n_Code is mostly written in accordance with [Google PEP style guide](https://google.github.io/styleguide/pyguide.html)_\n\n\n\n\n",
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