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    "info": {
        "author": "Alysson A Costa",
        "author_email": "alysson.avila.costa@gmail.com",
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            "License :: OSI Approved :: MIT License",
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        "description": "# simplega\n\n```simplega``` is a simple python implementation of genetic algorithm and it is available through PyPI.\n\n## Install\n```shell\npython3 -m pip install simplega\n```\n\n## Usage\n\nImport the package\n```python\nfrom simplega import Chromosome, Population, GA, GAHelper\n# or\nfrom simplega import *\n```\n\nCreate a fitness function that suits toyr problem\n```python\ndef maximize(chromosome):\n    return sum( [ ord(gene) for gene in chromosome.dna ] )\n```\n\nCreate a new instance of GA specifying the fitness function to be used\n```python\nga = GA(maximize)\n```\n\nPerform the steps of the genetic algorithm and retrieve the fittest chromosome\n```python\nga.run()\nprint(ga.get_fittest())\n```\n\nAll the script - really simple:\n```python\nfrom simplega import *\n\ndef maximize(chromosome):\n    return sum( [ ord(gene) for gene in chromosome.dna ] )\n\nga = GA(maximize)\nga.run()\nprint(ga.get_fittest())\n```\n\n### Advanced usage\n\nYou can customize your instance of GA, replacing any or all of its default values\n```python\nga = GA(fitness_function, \n  genes =  [ chr(n) for n in range(65,91) ], \n  chromosome_size =  10, \n  population_size =  100, \n  generations =  100, \n  crossover_points =  1, \n  elitism_rate =  0.05, \n  crossover_rate =  0.85, \n  mutation_rate =  0.01, \n  )\n```\n\nYou can print the fittest chromosome of each generation with ```ga.run(True)```\n\n## Contributing\n\nPlease submit bugfixes, enhancements, unit tests, usecases and examples with a pull request.\n\n",
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        "license": "MIT",
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        "name": "simplega",
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