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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 
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‎README.md

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# Donation
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For donattion:
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* By Card (recommended): https://donate.stripe.com/eVa5kO866elKgM0144
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* [Open Collective](https://opencollective.com/pygad): [opencollective.com/pygad](https://opencollective.com/pygad).
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* PayPal: Either this link: [paypal.me/ahmedfgad](https://paypal.me/ahmedfgad) or the e-mail address ahmed.f.gad@gmail.com.
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*[Credit/Debit Card](https://donate.stripe.com/eVa5kO866elKgM0144): https://donate.stripe.com/eVa5kO866elKgM0144
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* [Open Collective](https://opencollective.com/pygad): [opencollective.com/pygad](https://opencollective.com/pygad)
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* PayPal: Use either this link: [paypal.me/ahmedfgad](https://paypal.me/ahmedfgad) or the e-mail address ahmed.f.gad@gmail.com
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* Interac e-Transfer: Use e-mail address ahmed.f.gad@gmail.com
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# Installation
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function_inputs = [4,-2,3.5,5,-11,-4.7]
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desired_output = 44
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def fitness_func(solution, solution_idx):
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def fitness_func(ga_instance, solution, solution_idx):
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output = numpy.sum(solution*function_inputs)
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fitness = 1.0 / (numpy.abs(output - desired_output) + 0.000001)
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return fitness
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function_inputs = [4,-2,3.5,5,-11,-4.7] # Function inputs.
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desired_output = 44 # Function output.
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def fitness_func(solution, solution_idx):
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def fitness_func(ga_instance, solution, solution_idx):
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# Calculating the fitness value of each solution in the current population.
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# The fitness function calulates the sum of products between each input and its corresponding weight.
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output = numpy.sum(solution*function_inputs)
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- [KDnuggets](https://www.kdnuggets.com/2018/04/building-convolutional-neural-network-numpy-scratch.html)
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- [Chinese Translation](http://m.aliyun.com/yunqi/articles/585741)
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[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.
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[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.
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[![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)
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* [KDnuggets](https://kdnuggets.com/author/ahmed-gad)
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* [TowardsDataScience](https://towardsdatascience.com/@ahmedfgad)
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* [GitHub](https://github.com/ahmedfgad)
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‎__init__.py

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from .pygad import * # Relative import.
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__version__ = "2.19.2"
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__version__ = "3.0.0"

‎docs/source/Footer.rst

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