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Commit a912ec9

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PyGAD 2.19.2 documentation
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‎docs/source/Footer.rst

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summary of the PyGAD lifecycle.
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2. A new optional parameter called ``fitness_batch_size`` is supported
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to calculate the fitness function in batches. If it is assigned the
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value ``1`` or ``None`` (default), then the normal flow is used
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where the fitness function is called for each individual solution.
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If the ``fitness_batch_size`` parameter is assigned a value
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satisfying this condition ``1 < fitness_batch_size <= sol_per_pop``,
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then the solutions are grouped into batches of size
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``fitness_batch_size`` and the fitness function is called once for
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each batch. In this case, the fitness function must return a
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list/tuple/numpy.ndarray with a length equal to the number of
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solutions passed.
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to calculate the fitness in batches. If it is assigned the value
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``1`` or ``None`` (default), then the normal flow is used where the
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fitness function is called for each individual solution. If the
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``fitness_batch_size`` parameter is assigned a value satisfying this
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condition ``1 < fitness_batch_size <= sol_per_pop``, then the
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solutions are grouped into batches of size ``fitness_batch_size``
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and the fitness function is called once for each batch. In this
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case, the fitness function must return a list/tuple/numpy.ndarray
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with a length equal to the number of solutions passed.
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https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/136.
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3. The ``cloudpickle`` library
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1. Add the `cloudpickle <https://github.com/cloudpipe/cloudpickle>`__
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library as a dependency.
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.. _pygad-2192:
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PyGAD 2.19.2
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------------
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Release Data 23 February 2023
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1. Fix an issue when parallel processing was used where the elitism
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solutions' fitness values are not re-used.
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https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/160#issuecomment-1441718184
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PyGAD Projects at GitHub
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========================
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‎docs/source/conf.py

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author = 'Ahmed Fawzy Gad'
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# The full version, including alpha/beta/rc tags
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release = '2.19.1'
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release = '2.19.2'
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master_doc = 'index'
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