PyGAD 3.0.0 Release

PyGAD 3.0.0 Release Notes
1. The structure of the library is changed and some methods defined in the `pygad.py` module are moved to the `pygad.utils`, `pygad.helper`, and `pygad.visualize` submodules.
  2. The `pygad.utils.parent_selection` module has a class named `ParentSelection` where all the parent selection operators exist. The `pygad.GA` class extends this class.
  3. The `pygad.utils.crossover` module has a class named `Crossover` where all the crossover operators exist. The `pygad.GA` class extends this class.
  4. The `pygad.utils.mutation` module has a class named `Mutation` where all the mutation operators exist. The `pygad.GA` class extends this class.
  5. The `pygad.helper.unique` module has a class named `Unique` some helper methods exist to solve duplicate genes and make sure every gene is unique. The `pygad.GA` class extends this class.
  6. The `pygad.visualize.plot` module has a class named `Plot` where all the methods that create plots exist. The `pygad.GA` class extends this class.

```python
...
class GA(utils.parent_selection.ParentSelection,
         utils.crossover.Crossover,
         utils.mutation.Mutation,
         helper.unique.Unique,
         visualize.plot.Plot):
...
```

2. Support of using the `logging` module to log the outputs to both the console and text file instead of using the `print()` function. This is by assigning the `logging.Logger` to the new `logger` parameter. Check the [Logging Outputs](https://pygad.readthedocs.io/en/latest/README_pygad_ReadTheDocs.html#logging-outputs) for more information.
3. A new instance attribute called `logger` to save the logger.
4. The function/method passed to the `fitness_func` parameter accepts a new parameter that refers to the instance of the `pygad.GA` class. Check this for an example: [Use Functions and Methods to Build Fitness Function and Callbacks](https://pygad.readthedocs.io/en/latest/README_pygad_ReadTheDocs.html#use-functions-and-methods-to-build-fitness-and-callbacks). #163
5. Update the documentation to include an example of using functions and methods to calculate the fitness and build callbacks. Check this for more details: [Use Functions and Methods to Build Fitness Function and Callbacks](https://pygad.readthedocs.io/en/latest/README_pygad_ReadTheDocs.html#use-functions-and-methods-to-build-fitness-and-callbacks). #92 (comment)
6. Validate the value passed to the `initial_population` parameter.
7. Validate the type and length of the `pop_fitness` parameter of the `best_solution()` method.
8. Some edits in the documentation. #106
9. Fix an issue when building the initial population as (some) genes have their value taken from the mutation range (defined by the parameters `random_mutation_min_val` and `random_mutation_max_val`) instead of using the parameters `init_range_low` and `init_range_high`.
10. The `summary()` method returns the summary as a single-line string. Just log/print the returned string it to see it properly.
11. The `callback_generation` parameter is removed. Use the `on_generation` parameter instead.
12. There was an issue when using the `parallel_processing` parameter with Keras and PyTorch. As Keras/PyTorch are not thread-safe, the `predict()` method gives incorrect and weird results when more than 1 thread is used. #145 ahmedfgad/TorchGA#5 ahmedfgad/KerasGA#6. Thanks to this [StackOverflow answer](https://stackoverflow.com/a/75606666/5426539).
13. Replace `numpy.float` by `float` in the 2 parent selection operators roulette wheel and stochastic universal. #168

Author: ahmedfgad

README.md

@@ -14,10 +14,10 @@ The library is under active development and more features are added regularly. I
 
 # Donation
 
-For donattion:
-* By Card (recommended): https://donate.stripe.com/eVa5kO866elKgM0144
-* [Open Collective](https://opencollective.com/pygad): [opencollective.com/pygad](https://opencollective.com/pygad). 
-* PayPal: Either this link: [paypal.me/ahmedfgad](https://paypal.me/ahmedfgad) or the e-mail address [email protected].
+* [Credit/Debit Card](https://donate.stripe.com/eVa5kO866elKgM0144): https://donate.stripe.com/eVa5kO866elKgM0144
+* [Open Collective](https://opencollective.com/pygad): [opencollective.com/pygad](https://opencollective.com/pygad)
+* PayPal: Use either this link: [paypal.me/ahmedfgad](https://paypal.me/ahmedfgad) or the e-mail address [email protected]
+* Interac e-Transfer: Use e-mail address [email protected]
 
 # Installation
 
@@ -76,7 +76,7 @@ import numpy
 function_inputs = [4,-2,3.5,5,-11,-4.7]
 desired_output = 44
 
-def fitness_func(solution, solution_idx):
+def fitness_func(ga_instance, solution, solution_idx):
     output = numpy.sum(solution*function_inputs)
     fitness = 1.0 / (numpy.abs(output - desired_output) + 0.000001)
     return fitness
@@ -164,7 +164,7 @@ What are the best values for the 6 weights (w1 to w6)? We are going to use the g
 function_inputs = [4,-2,3.5,5,-11,-4.7] # Function inputs.
 desired_output = 44 # Function output.
 
-def fitness_func(solution, solution_idx):
+def fitness_func(ga_instance, solution, solution_idx):
     # Calculating the fitness value of each solution in the current population.
     # The fitness function calulates the sum of products between each input and its corresponding weight.
     output = numpy.sum(solution*function_inputs)
@@ -280,7 +280,7 @@ To start with coding the genetic algorithm, you can check the tutorial titled [*
 - [KDnuggets](https://www.kdnuggets.com/2018/04/building-convolutional-neural-network-numpy-scratch.html)
 - [Chinese Translation](http://m.aliyun.com/yunqi/articles/585741)
 
-[This tutorial](https://www.linkedin.com/pulse/building-convolutional-neural-network-using-numpy-from-ahmed-gad) is prepared based on a previous version of the project but it still a good resource to start with coding CNNs.
+[This tutorial](https://www.linkedin.com/pulse/building-convolutional-neural-network-using-numpy-from-ahmed-gad)) is prepared based on a previous version of the project but it still a good resource to start with coding CNNs.
 
 [![Building CNN in Python](https://user-images.githubusercontent.com/16560492/82431022-6c3a1200-9a8e-11ea-8f1b-b055196d76e3.png)](https://www.linkedin.com/pulse/building-convolutional-neural-network-using-numpy-from-ahmed-gad)
 
@@ -331,4 +331,3 @@ If you used PyGAD, please consider adding a citation to the following paper abou
 * [KDnuggets](https://kdnuggets.com/author/ahmed-gad)
 * [TowardsDataScience](https://towardsdatascience.com/@ahmedfgad)
 * [GitHub](https://github.com/ahmedfgad)
-

__init__.py

@@ -1,3 +1,3 @@
 from .pygad import * # Relative import.
 
-__version__ = "2.19.2"
+__version__ = "3.0.0"

docs/source/Footer.rst

@@ -852,7 +852,7 @@ Release Date: 28 September 2021
    equation_inputs = [4,-2,3.5]
    desired_output = 44
 
-   def fitness_func(solution, solution_idx):
+   def fitness_func(ga_instance, solution, solution_idx):
        output = numpy.sum(solution * equation_inputs)
        fitness = 1.0 / (numpy.abs(output - desired_output) + 0.000001)
        return fitness
@@ -891,7 +891,7 @@ progress bar.
    equation_inputs = [4,-2,3.5]
    desired_output = 44
 
-   def fitness_func(solution, solution_idx):
+   def fitness_func(ga_instance, solution, solution_idx):
        output = numpy.sum(solution * equation_inputs)
        fitness = 1.0 / (numpy.abs(output - desired_output) + 0.000001)
        return fitness
@@ -1119,7 +1119,7 @@ Release Date: 22 February 2023
     (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
+    function). The ``cloudpickle`` library is added as a dependency in
     the ``requirements.txt`` file.
     https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/159
 
@@ -1185,12 +1185,107 @@ Release Date: 22 February 2023
 PyGAD 2.19.2
 ------------
 
-Release Data 23 February 2023
+Release Date 23 February 2023
 
 1. Fix an issue when parallel processing was used where the elitism
    solutions' fitness values are not re-used.
    https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/160#issuecomment-1441718184
 
+.. _pygad-300:
+
+PyGAD 3.0.0
+-----------
+
+Release Date ... 2023
+
+1. The structure of the library is changed and some methods defined in
+   the ``pygad.py`` module are moved to the ``pygad.utils``,
+   ``pygad.helper``, and ``pygad.visualize`` submodules.
+
+2. The ``pygad.utils.parent_selection`` module has a class named
+   ``ParentSelection`` where all the parent selection operators exist.
+
+3. The ``pygad.utils.crossover`` module has a class named ``Crossover``
+   where all the crossover operators exist.
+
+4. The ``pygad.utils.mutation`` module has a class named ``Mutation``
+   where all the mutation operators exist.
+
+5. The ``pygad.helper.unique`` module has a class named ``Unique`` some
+   helper methods exist to solve duplicate genes and make sure every
+   gene is unique.
+
+6. | The ``pygad.visualize.plot`` module has a class named ``Plot``
+     where all the methods that create plots exist.
+   | The ``pygad.GA`` class extends all of these classes.
+
+.. code:: python
+
+   ...
+   class GA(utils.parent_selection.ParentSelection, 
+            utils.crossover.Crossover, 
+            utils.mutation.Mutation, 
+            helper.unique.Unique,
+            visualize.plot.Plot):
+   ...
+
+1.  Support of using the ``logging`` module to log the outputs to both
+    the console and text file instead of using the ``print()`` function.
+    This is by assigning the ``logging.Logger`` to the new ``logger``
+    parameter. Check the `Logging
+    Outputs <https://pygad.readthedocs.io/en/latest/README_pygad_ReadTheDocs.html#logging-outputs>`__
+    for more information.
+
+2.  A new instance attribute called ``logger`` to save the logger.
+
+3.  The function/method passed to the ``fitness_func`` parameter accepts
+    a new parameter that refers to the instance of the ``pygad.GA``
+    class. Check this for an example: `Use Functions and Methods to
+    Build Fitness Function and
+    Callbacks <https://pygad.readthedocs.io/en/latest/README_pygad_ReadTheDocs.html#use-functions-and-methods-to-build-fitness-and-callbacks>`__.
+    https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/163
+
+4.  Update the documentation to include an example of using functions
+    and methods to calculate the fitness and build callbacks. Check this
+    for more details: `Use Functions and Methods to Build Fitness
+    Function and
+    Callbacks <https://pygad.readthedocs.io/en/latest/README_pygad_ReadTheDocs.html#use-functions-and-methods-to-build-fitness-and-callbacks>`__.
+    https://github.com/ahmedfgad/GeneticAlgorithmPython/pull/92#issuecomment-1443635003
+
+5.  Validate the value passed to the ``initial_population`` parameter.
+
+6.  Validate the type and length of the ``pop_fitness`` parameter of the
+    ``best_solution()`` method.
+
+7.  Some edits in the documentation.
+    https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/106
+
+8.  Fix an issue when building the initial population as (some) genes
+    have their value taken from the mutation range (defined by the
+    parameters ``random_mutation_min_val`` and
+    ``random_mutation_max_val``) instead of using the parameters
+    ``init_range_low`` and ``init_range_high``.
+
+9.  The ``summary()`` method returns the summary as a single-line
+    string. Just log/print the returned string it to see it properly.
+
+10. The ``callback_generation`` parameter is removed. Use the
+    ``on_generation`` parameter instead.
+
+11. There was an issue when using the ``parallel_processing`` parameter
+    with Keras and PyTorch. As Keras/PyTorch are not thread-safe, the
+    ``predict()`` method gives incorrect and weird results when more
+    than 1 thread is used.
+    https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/145
+    https://github.com/ahmedfgad/TorchGA/issues/5
+    https://github.com/ahmedfgad/KerasGA/issues/6. Thanks to this
+    `StackOverflow
+    answer <https://stackoverflow.com/a/75606666/5426539>`__.
+
+12. Replace ``numpy.float`` by ``float`` in the 2 parent selection
+    operators roulette wheel and stochastic universal.
+    https://github.com/ahmedfgad/GeneticAlgorithmPython/pull/168
+
 PyGAD Projects at GitHub
 ========================