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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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+
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+ PyGAD 2.19.2
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+ ------------
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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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+
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PyGAD Projects at GitHub
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========================
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