vishalbelsare · pull · Apr 8, 2023 · Apr 8, 2023 · Apr 8, 2023 · Apr 8, 2023
diff --git a/.github/FUNDING.yml b/.github/FUNDING.yml
@@ -1,12 +1,12 @@
 # These are supported funding model platforms
 
 github: # Replace with up to 4 GitHub Sponsors-enabled usernames e.g., [user1, user2]
-PayPal: http://paypal.me/ahmedfgad # Replace with a single Patreon username
+# paypal: http://paypal.me/ahmedfgad # Replace with a single Patreon username
 open_collective: pygad
 ko_fi: # Replace with a single Ko-fi username
 tidelift: # Replace with a single Tidelift platform-name/package-name e.g., npm/babel
 community_bridge: # Replace with a single Community Bridge project-name e.g., cloud-foundry
 liberapay: # Replace with a single Liberapay username
 issuehunt: # Replace with a single IssueHunt username
 otechie: # Replace with a single Otechie username
-custom: https://donate.stripe.com/eVa5kO866elKgM0144 # Replace with up to 4 custom sponsorship URLs e.g., ['link1', 'link2']
+custom: ['https://donate.stripe.com/eVa5kO866elKgM0144', 'http://paypal.me/ahmedfgad'] # Replace with up to 4 custom sponsorship URLs e.g., ['link1', 'link2']
diff --git a/README.md b/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 ahmed.f.gad@gmail.com.
+* [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 ahmed.f.gad@gmail.com
+* Interac e-Transfer: Use e-mail address ahmed.f.gad@gmail.com
 
 # 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)
-
diff --git a/__init__.py b/__init__.py
@@ -1,3 +1,3 @@
 from .pygad import * # Relative import.
 
-__version__ = "2.19.2"
+__version__ = "3.0.0"
diff --git a/docs/source/Footer.rst b/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 +1185,100 @@ 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 8 April 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.
+    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.
+
+7.  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.
+
+8.  A new instance attribute called ``logger`` to save the logger.
+
+9.  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
+
+10. 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
+
+11. Validate the value passed to the ``initial_population`` parameter.
+
+12. Validate the type and length of the ``pop_fitness`` parameter of the
+    ``best_solution()`` method.
+
+13. Some edits in the documentation.
+    https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/106
+
+14. 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``.
+
+15. The ``summary()`` method returns the summary as a single-line
+    string. Just log/print the returned string it to see it properly.
+
+16. The ``callback_generation`` parameter is removed. Use the
+    ``on_generation`` parameter instead.
+
+17. 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>`__.
+
+18. 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
 ========================