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        "author": "Justin Lovinger",
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        "description": "Optimal (beta)\n==============\n\nA python metaheuristic optimization library. Built for easy extension\nand usage.\n\nWarning: Optimal is in beta. API may change. I will do my best to note\nany breaking changes in this readme, but no guarantee is given.\n\nSupported metaheuristics:\n\n-  Genetic algorithms (GA)\n-  Gravitational search algorithm (GSA)\n-  Cross entropy (CE)\n\nInstallation\n============\n\n::\n\n    pip install optimal\n\nUsage\n=====\n\n::\n\n    import math\n\n    from optimal import GenAlg\n    from optimal import Problem\n    from optimal import helpers\n\n    # The genetic algorithm uses binary solutions.\n    # A decode function is useful for converting the binary solution to real numbers\n    def decode_ackley(binary):\n        # Helpful functions from helpers are used to convert binary to float\n        # x1 and x2 range from -5.0 to 5.0\n        x1 = helpers.binary_to_float(binary[0:16], -5.0, 5.0)\n        x2 = helpers.binary_to_float(binary[16:32], -5.0, 5.0)\n        return x1, x2\n\n    # ackley is our fitness function\n    # This is how a user defines the goal of their problem\n    def ackley_fitness(solution):\n        x1, x2 = solution\n\n        # Ackley's function\n        # A common mathematical optimization problem\n        output = -20 * math.exp(-0.2 * math.sqrt(0.5 * (x1**2 + x2**2))) - math.exp(\n            0.5 * (math.cos(2 * math.pi * x1) + math.cos(2 * math.pi * x2))) + 20 + math.e\n\n        # You can prematurely stop the metaheuristic by returning True\n        # as the second return value\n        # Here, we consider the problem solved if the output is <= 0.01\n        finished = output <= 0.01\n\n        # Because this function is trying to minimize the output,\n        # a smaller output has a greater fitness\n        fitness = 1 / output\n\n        # First return argument must be a real number\n        # The higher the number, the better the solution\n        # Second return argument is a boolean, and optional\n        return fitness, finished\n\n    # Define a problem instance to optimize\n    # We can optionally include a decode function\n    # The optimizer will pass the decoded solution into your fitness function\n    # Additional fitness function and decode function parameters can also be added\n    ackley = Problem(ackley_fitness, decode_function=decode_ackley)\n\n    # Create a genetic algorithm with a chromosome size of 32,\n    # and use it to solve our problem\n    my_genalg = GenAlg(32)\n    best_solution = my_genalg.optimize(ackley)\n\n    print best_solution\n\nImportant notes:\n\n-  Fitness function must take solution as its first argument\n-  Fitness function must return a real number as its first return value\n\nFor further usage details, see comprehensive doc strings.\n\nMajor Changes\n=============\n\n08/27/2017\n----------\n\nMoved a number of options from Optimizer to Optimizer.optimize\n\n07/26/2017\n----------\n\nRenamed common.random\\_solution\\_binary to\ncommon.random\\_binary\\_solution, and common.random\\_solution\\_real to\ncommon.random\\_real\\_solution\n\n11/10/2016\n----------\n\nproblem now an argument of Optimizer.optimize, instead of\nOptimizer.\\_\\_init\\_\\_.\n\n11/10/2016\n----------\n\nmax\\_iterations now an argument of Optimizer.optimize, instead of\nOptimizer.\\_\\_init\\_\\_.\n\n11/8/2016\n---------\n\nOptimizer now takes a problem instance, instead of a fitness function\nand kwargs.\n\n11/5/2016\n---------\n\nLibrary reorganized with greater reliance on \\_\\_init\\_\\_.py.\n\nOptimizers can now be imported with:\n\n::\n\n    from optimal import GenAlg, GSA, CrossEntropy\n\nEtc.",
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        "home_page": "https://github.com/JustinLovinger/Optimal",
        "keywords": "optimization,metaheuristic,genetic algorithm,GA,gravitational search algorithm,GSA,cross entropy",
        "license": "MIT",
        "maintainer": "",
        "maintainer_email": "",
        "name": "optimal",
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        "summary": "A python metaheuristic optimization library. Currently supports Genetic Algorithms, Gravitational Search, and Cross Entropy.",
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