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        "author": "Chris Bamford",
        "author_email": "chrisbam4d@gmail.com",
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            "License :: OSI Approved :: GNU General Public License v3 (GPLv3)",
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        "description": "# Rolling Horizon Evolutionary Algorithm\n\n[![PyPI version](https://badge.fury.io/py/RollingHorizonEA.svg)](https://badge.fury.io/py/RollingHorizonEA)\n\nAn implementation of the [Rolling Horizon Evolutionary Algorithm](https://www.semanticscholar.org/paper/Rolling-horizon-evolution-versus-tree-search-for-in-Liebana-Samothrakis/0cff838805be4b6366756a553daca0036778c1e0)\n\n## Installation\n\n### using pip\n\n```\npip install RollingHorizonEA\n```\n\n## Usage\n\nTo use the rolling horizon evolutionary algorithm, you will need your game class to implement the `Environment` interface.\n\n### Examples\n\nExamples of setting up any game environment can be found in the `examples` directory and run with:\n```\npython run.py\n```\n\n#### m_max example\n\n```\nnum_dims = 600\nm = 50\nnum_evals = 50\nrollout_length = 10\nmutation_probability = 0.1\n\n# Set up the problem domain as m-max game\nenvironment = MMaxGame(num_dims, m)\n\nrhea = RollingHorizonEvolutionaryAlgorithm(rollout_length, environment, mutation_probability, num_evals)\n\nrhea.run()\n```\n\n\n## Cite\n\nIf you want to cite this library, please use the following DOI\n\n[![DOI](https://zenodo.org/badge/172040305.svg)](https://zenodo.org/badge/latestdoi/172040305)\n\n\n",
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