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Release History
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===============
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PyGAD 1.0.17
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------------
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values for the solutions. This allows the project to be customized to
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any problem by building the right fitness function.
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PyGAD 1.0.20
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-------------
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4. The code object ``__code__ `` of the passed fitness function is
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checked to ensure it has the right number of parameters.
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PyGAD 2.0.0
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is called after each generation. This helps the user to do
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post-processing or debugging operations after each generation.
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PyGAD 2.1.0
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-----------
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2. Mutation is applied independently for the genes.
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PyGAD 2.2.1
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1. Adding 2 extra modules (pygad.nn and pygad.gann) for building and
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training neural networks with the genetic algorithm.
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PyGAD 2.2.2
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``crossover_type `` parameters of the pygad.GA class constructor. When
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``None ``, this means the step is bypassed and has no action.
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PyGAD 2.3.0
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@@ -166,7 +166,7 @@ Release date: 1 June 2020
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6. The name of the ``pygad.nn.train_network() `` function is changed to
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``pygad.nn.train() ``.
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PyGAD 2.4.0
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if ga_instance.best_solution()[1 ] >= 70 :
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return " stop"
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PyGAD 2.5.0
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randomly based on the ``gene_space `` parameter. Moreover, the mutation
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is applied based on this parameter.
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PyGAD 2.6.0
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``on_fitness ``, ``on_parents ``, ``on_crossover ``, ``on_mutation ``,
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``on_generation ``, and ``on_stop ``.
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PyGAD 2.7.0
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case, the activation function of the last layer can be set to any type
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(e.g. softmax).
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PyGAD 2.7.1
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@@ -387,7 +387,7 @@ Release Date: 11 September 2020
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1. A bug fix when the ``problem_type `` argument is set to
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``regression ``.
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PyGAD 2.7.2
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1. Bug fix to support building and training regression neural networks
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with multiple outputs.
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PyGAD 2.8.0
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1. Support of a new module named ``kerasga `` so that the Keras models
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can be trained by the genetic algorithm using PyGAD.
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PyGAD 2.8.1
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Management, Faculty of Engineering, Alexandria University,
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Egypt <https://www.linkedin.com/in/hamadakassem> `__.
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PyGAD 2.9.0
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``numpy.int64 ``, ``numpy.float ``, ``numpy.float16 ``,
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``numpy.float32 ``, or ``numpy.float64 ``.
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PyGAD 2.10.0
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``cal_pop_fitness() `` method is called to calculate the fitness
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values of the population.
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PyGAD 2.10.1
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pointing about that at
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`GitHub <https://github.com/ahmedfgad/KerasGA/issues/1 >`__.
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+ PyGAD 2.10.2
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+ ------------
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+ Release Date: 15 January 2021
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+ 1. A bug fix when ``save_best_solutions=True ``. Refer to this issue for
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+ more information:
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+ https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/25
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PyGAD Projects at GitHub
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========================
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open-source GitHub projects. A brief note about these projects is given
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in the next subsections.
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`GeneticAlgorithmPython <https://github.com/ahmedfgad/GeneticAlgorithmPython >`__
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--------------------------------------------------------------------------------
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is the first project which is an open-source Python 3 project for
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implementing the genetic algorithm based on NumPy.
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`NumPyANN <https://github.com/ahmedfgad/NumPyANN >`__
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----------------------------------------------------
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supports classification and later regression will be also supported.
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Moreover, only one class is supported per sample.
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`NeuralGenetic <https://github.com/ahmedfgad/NeuralGenetic >`__
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--------------------------------------------------------------
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`GeneticAlgorithmPython <https://github.com/ahmedfgad/GeneticAlgorithmPython >`__
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and `NumPyANN <https://github.com/ahmedfgad/NumPyANN >`__.
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`NumPyCNN <https://github.com/ahmedfgad/NumPyCNN >`__
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----------------------------------------------------
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is to only implement the **forward pass ** of a convolutional neural
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network without using a training algorithm.
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`CNNGenetic <https://github.com/ahmedfgad/CNNGenetic >`__
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--------------------------------------------------------
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`GeneticAlgorithmPython <https://github.com/ahmedfgad/GeneticAlgorithmPython >`__
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project for building the genetic algorithm.
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`KerasGA <https://github.com/ahmedfgad/KerasGA >`__
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--------------------------------------------------
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`GeneticAlgorithmPython <https://github.com/ahmedfgad/GeneticAlgorithmPython >`__
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project for building the genetic algorithm.
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`TorchGA <https://github.com/ahmedfgad/TorchGA >`__
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--------------------------------------------------
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`pygad.torchga <https://github.com/ahmedfgad/TorchGA >`__:
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https://github.com/ahmedfgad/TorchGA
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Submitting Issues
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=================
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If this is not a proper option for you, then check the **Contact Us **
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section for more contact details.
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Ask for Feature
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===============
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Also check the **Contact Us ** section for more contact details.
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Projects Built using PyGAD
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==========================
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- Preferably, a link that directs the readers to your project
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For More Information
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====================
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There are different resources that can be used to get started with the
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genetic algorithm and building it in Python.
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Tutorial: Implementing Genetic Algorithm in Python
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--------------------------------------------------
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|image0 |
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Tutorial: Introduction to Genetic Algorithm
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-------------------------------------------
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|image1 |
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Tutorial: Build Neural Networks in Python
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-----------------------------------------
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|image2 |
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Tutorial: Optimize Neural Networks with Genetic Algorithm
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|image3 |
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Tutorial: Building CNN in Python
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|image4 |
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Tutorial: Derivation of CNN from FCNN
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|image5 |
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Book: Practical Computer Vision Applications Using Deep Learning with CNNs
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.. figure :: https://user-images.githubusercontent.com/16560492/78830077-ae7c2800-79e7-11ea-980b-53b6bd879eeb.jpg
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:alt:
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Contact Us
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==========
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