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‎docs/source/Footer.rst

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1. The ``best_solution_fitness`` attribute is renamed to
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``best_solutions_fitness`` (plural solution).
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2. Mutation is applied independently for the genes.
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PyGAD 2.2.1
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===========
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-----------
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Release Date: 17 May 2020
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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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-----------
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Release Date: 18 May 2020
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1. The initial value of the ``generations_completed`` attribute of
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instances from the pygad.GA class is ``0`` rather than ``None``.
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2. An optional bool parameter named ``mutation_by_replacement`` is added
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to the constructor of the pygad.GA class. It works only when the
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selected type of mutation is random (``mutation_type="random"``). In
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this case, setting ``mutation_by_replacement=True`` means replace the
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gene by the randomly generated value. If ``False``, then it has no
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effect and random mutation works by adding the random value to the
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gene. This parameter should be used when the gene falls within a
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fixed range and its value must not go out of this range. Here are
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some examples:
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Assume there is a gene with the value 0.5.
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If ``mutation_type="random"`` and ``mutation_by_replacement=False``,
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then the generated random value (e.g. 0.1) will be added to the gene
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value. The new gene value is **0.5+0.1=0.6**.
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If ``mutation_type="random"`` and ``mutation_by_replacement=True``,
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then the generated random value (e.g. 0.1) will replace the gene
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value. The new gene value is **0.1**.
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3. ``None`` value could be assigned to the ``mutation_type`` and
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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 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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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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|image2|
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Tutorial: Optimize Neural Networks with Genetic Algorithm
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Book: Practical Computer Vision Applications Using Deep Learning with CNNs
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--------------------------------------------------------------------------
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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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