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‎docs/source/Footer.rst

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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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2020
PyGAD 1.0.20
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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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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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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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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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PyGAD 2.7.1
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1. A bug fix when the ``problem_type`` argument is set to
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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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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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created to calculate the average fitness value used in adaptive
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mutation to filter the solutions.
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11. The ``best_solution()`` method accepts a new optional parameter
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called ``pop_fitness``. It accepts a list of the fitness values of
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the solutions in the population. If ``None``, then the
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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 Projects at GitHub
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open-source GitHub projects. A brief note about these projects is given
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`GeneticAlgorithmPython<https://github.com/ahmedfgad/GeneticAlgorithmPython>`__
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is the first project which is an open-source Python 3 project for
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`NumPyANN<https://github.com/ahmedfgad/NumPyANN>`__
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`NeuralGenetic<https://github.com/ahmedfgad/NeuralGenetic>`__
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`GeneticAlgorithmPython<https://github.com/ahmedfgad/GeneticAlgorithmPython>`__
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`NumPyCNN<https://github.com/ahmedfgad/NumPyCNN>`__
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`CNNGenetic<https://github.com/ahmedfgad/CNNGenetic>`__
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`KerasGA<https://github.com/ahmedfgad/KerasGA>`__
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`TorchGA<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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Ask for Feature
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Projects Built using PyGAD
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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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Tutorial: Introduction to Genetic Algorithm
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Tutorial: Build Neural Networks in Python
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Tutorial: Optimize Neural Networks with Genetic Algorithm
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Tutorial: Building CNN in Python
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Tutorial: Derivation of CNN from FCNN
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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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