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)

Author: ahmedfgad

README.md

@@ -64,7 +64,7 @@ Please check the **Contact Us** section for more contact details.
 
 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`.
 
-![PyGAD Lifecycle](https://user-images.githubusercontent.com/16560492/89446279-9c6f8380-d754-11ea-83fd-a60ea2f53b85.jpg)
+![PyGAD Lifecycle](https://user-images.githubusercontent.com/16560492/220486073-c5b6089d-81e4-44d9-a53c-385f479a7273.jpg)
 
 The next code implements all the callback functions to trace the execution of the genetic algorithm. Each callback function prints its name.
 

__init__.py

@@ -1,3 +1,3 @@
 from .pygad import * # Relative import.
 
-__version__ = "2.18.3"
+__version__ = "2.19.0"

docs/source/Footer.rst

@@ -1093,6 +1093,84 @@ Release Date: 14 February 2023
 
 1. Bug fixes.
 
+.. _pygad-2190:
+
+PyGAD 2.19.0
+------------
+
+Release Date: 22 February 2023
+
+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.
+    https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/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.
+    https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/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``.
+    https://github.com/ahmedfgad/GeneticAlgorithmPython/pull/92
+    https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/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``.
+    https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/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.
+    https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/154#issuecomment-1438739483
+
+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.
+    https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/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.
+    https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/79#issuecomment-1439605442
+
 PyGAD Projects at GitHub
 ========================