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Commit ed1199d

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PyGAD 2.19.0 Release
PyGAD 2.19.0 Release Notes 1. A new `summary()` method is supported to return a Keras-like summary of the PyGAD lifecycle. 2. A new optional parameter called `fitness_batch_size` is supported to calculate the fitness function in batches. If it is assigned the value `1` or `None` (default), then the normal flow is used where the fitness function is called for each individual solution. If the `fitness_batch_size` parameter is assigned a value satisfying this condition `1 < fitness_batch_size <= sol_per_pop`, then the solutions are grouped into batches of size `fitness_batch_size` and the fitness function is called once for each batch. In this case, the fitness function must return a list/tuple/numpy.ndarray with a length equal to the number of solutions passed. #136. 3. The `cloudpickle` library (https://github.com/cloudpipe/cloudpickle) is used instead of the `pickle` library to pickle the `pygad.GA` objects. This solves the issue of having to redefine the functions (e.g. fitness function). The `cloudpickle` library is added as a dependancy in the `requirements.txt` file. #159 4. Support of assigning methods to these parameters: `fitness_func`, `crossover_type`, `mutation_type`, `parent_selection_type`, `on_start`, `on_fitness`, `on_parents`, `on_crossover`, `on_mutation`, `on_generation`, and `on_stop`. #92 #138 5. Validating the output of the parent selection, crossover, and mutation functions. 6. The built-in parent selection operators return the parent's indices as a NumPy array. 7. The outputs of the parent selection, crossover, and mutation operators must be NumPy arrays. 8. Fix an issue when `allow_duplicate_genes=True`. #39 9. Fix an issue creating scatter plots of the solutions' fitness. 10. Sampling from a `set()` is no longer supported in Python 3.11. Instead, sampling happens from a `list()`. Thanks `Marco Brenna` for pointing to this issue. 11. The lifecycle is updated to reflect that the new population's fitness is calculated at the end of the lifecycle not at the beginning. #154 (comment) 12. There was an issue when `save_solutions=True` that causes the fitness function to be called for solutions already explored and have their fitness pre-calculated. #160 13. A new instance attribute named `last_generation_elitism_indices` added to hold the indices of the selected elitism. This attribute helps to re-use the fitness of the elitism instead of calling the fitness function. 14. Fewer calls to the `best_solution()` method which in turns saves some calls to the fitness function. 15. Some updates in the documentation to give more details about the `cal_pop_fitness()` method. #79 (comment) 
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‎README.md

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The next figure lists the different stages in the lifecycle of an instance of the `pygad.GA` class. Note that PyGAD stops when either all generations are completed or when the function passed to the `on_generation` parameter returns the string `stop`.
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![PyGAD Lifecycle](https://user-images.githubusercontent.com/16560492/89446279-9c6f8380-d754-11ea-83fd-a60ea2f53b85.jpg)
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![PyGAD Lifecycle](https://user-images.githubusercontent.com/16560492/220486073-c5b6089d-81e4-44d9-a53c-385f479a7273.jpg)
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The next code implements all the callback functions to trace the execution of the genetic algorithm. Each callback function prints its name.
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‎__init__.py

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from .pygad import * # Relative import.
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__version__ = "2.18.3"
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__version__ = "2.19.0"

‎docs/source/Footer.rst

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1. Bug fixes.
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.. _pygad-2190:
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PyGAD 2.19.0
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------------
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Release Date: 22 February 2023
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1. A new ``summary()`` method is supported to return a Keras-like
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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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https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/136.
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3. The ``cloudpickle`` library
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(https://github.com/cloudpipe/cloudpickle) is used instead of the
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``pickle`` library to pickle the ``pygad.GA`` objects. This solves
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the issue of having to redefine the functions (e.g. fitness
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function). The ``cloudpickle`` library is added as a dependancy in
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the ``requirements.txt`` file.
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https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/159
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4. Support of assigning methods to these parameters: ``fitness_func``,
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``crossover_type``, ``mutation_type``, ``parent_selection_type``,
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``on_start``, ``on_fitness``, ``on_parents``, ``on_crossover``,
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``on_mutation``, ``on_generation``, and ``on_stop``.
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https://github.com/ahmedfgad/GeneticAlgorithmPython/pull/92
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https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/138
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5. Validating the output of the parent selection, crossover, and
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mutation functions.
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6. The built-in parent selection operators return the parent's indices
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as a NumPy array.
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7. The outputs of the parent selection, crossover, and mutation
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operators must be NumPy arrays.
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8. Fix an issue when ``allow_duplicate_genes=True``.
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https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/39
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9. Fix an issue creating scatter plots of the solutions' fitness.
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10. Sampling from a ``set()`` is no longer supported in Python 3.11.
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Instead, sampling happens from a ``list()``. Thanks ``Marco Brenna``
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for pointing to this issue.
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11. The lifecycle is updated to reflect that the new population's
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fitness is calculated at the end of the lifecycle not at the
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beginning.
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https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/154#issuecomment-1438739483
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12. There was an issue when ``save_solutions=True`` that causes the
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fitness function to be called for solutions already explored and
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have their fitness pre-calculated.
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https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/160
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13. A new instance attribute named ``last_generation_elitism_indices``
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added to hold the indices of the selected elitism. This attribute
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helps to re-use the fitness of the elitism instead of calling the
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fitness function.
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14. Fewer calls to the ``best_solution()`` method which in turns saves
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some calls to the fitness function.
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15. Some updates in the documentation to give more details about the
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``cal_pop_fitness()`` method.
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https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/79#issuecomment-1439605442
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PyGAD Projects at GitHub
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========================
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