PyGAD 2.10.0 Documentation

Documentation of pygad.torchga added.

Author: ahmedfgad

docs/source/Footer.rst

@@ -1,9 +1,9 @@
-.. _header-n0:
+.. _header-n344:
 
 Release History
 ===============
 
-.. _header-n2:
+.. _header-n346:
 
 PyGAD 1.0.17
 ------------
@@ -15,7 +15,7 @@ Release Date: 15 April 2020
    values for the solutions. This allows the project to be customized to
    any problem by building the right fitness function.
 
-.. _header-n7:
+.. _header-n351:
 
 PyGAD 1.0.20 
 -------------
@@ -35,7 +35,7 @@ Release Date: 4 May 2020
 4. The code object ``__code__`` of the passed fitness function is
    checked to ensure it has the right number of parameters.
 
-.. _header-n18:
+.. _header-n362:
 
 PyGAD 2.0.0 
 ------------
@@ -61,7 +61,7 @@ Release Date: 13 May 2020
    is called after each generation. This helps the user to do
    post-processing or debugging operations after each generation.
 
-.. _header-n29:
+.. _header-n373:
 
 PyGAD 2.1.0
 -----------
@@ -97,7 +97,7 @@ Release Date: 14 May 2020
 
 2. Mutation is applied independently for the genes.
 
-.. _header-n44:
+.. _header-n388:
 
 PyGAD 2.2.1
 -----------
@@ -107,7 +107,7 @@ Release Date: 17 May 2020
 1. Adding 2 extra modules (pygad.nn and pygad.gann) for building and
    training neural networks with the genetic algorithm.
 
-.. _header-n49:
+.. _header-n393:
 
 PyGAD 2.2.2
 -----------
@@ -141,7 +141,7 @@ The new gene value is **0.1**.
    ``crossover_type`` parameters of the pygad.GA class constructor. When
    ``None``, this means the step is bypassed and has no action.
 
-.. _header-n62:
+.. _header-n406:
 
 PyGAD 2.3.0
 -----------
@@ -166,7 +166,7 @@ Release date: 1 June 2020
 6. The name of the ``pygad.nn.train_network()`` function is changed to
    ``pygad.nn.train()``.
 
-.. _header-n77:
+.. _header-n421:
 
 PyGAD 2.4.0
 -----------
@@ -204,7 +204,7 @@ through more generations because no further improvement is possible.
        if ga_instance.best_solution()[1] >= 70:
            return "stop"
 
-.. _header-n87:
+.. _header-n431:
 
 PyGAD 2.5.0
 -----------
@@ -300,7 +300,7 @@ If the user did not assign the initial population to the
 randomly based on the ``gene_space`` parameter. Moreover, the mutation
 is applied based on this parameter.
 
-.. _header-n115:
+.. _header-n459:
 
 PyGAD 2.6.0 
 ------------
@@ -318,7 +318,7 @@ Release Date: 6 August 2020
    ``on_fitness``, ``on_parents``, ``on_crossover``, ``on_mutation``,
    ``on_generation``, and ``on_stop``.
 
-.. _header-n124:
+.. _header-n468:
 
 PyGAD 2.7.0
 -----------
@@ -377,7 +377,7 @@ parameter or set it to ``"classification"`` (default value). In this
 case, the activation function of the last layer can be set to any type
 (e.g. softmax).
 
-.. _header-n148:
+.. _header-n492:
 
 PyGAD 2.7.1
 -----------
@@ -387,7 +387,7 @@ Release Date: 11 September 2020
 1. A bug fix when the ``problem_type`` argument is set to
    ``regression``.
 
-.. _header-n153:
+.. _header-n497:
 
 PyGAD 2.7.2
 -----------
@@ -397,7 +397,7 @@ Release Date: 14 September 2020
 1. Bug fix to support building and training regression neural networks
    with multiple outputs.
 
-.. _header-n158:
+.. _header-n502:
 
 PyGAD 2.8.0
 -----------
@@ -407,7 +407,7 @@ Release Date: 20 September 2020
 1. Support of a new module named ``kerasga`` so that the Keras models
    can be trained by the genetic algorithm using PyGAD.
 
-.. _header-n163:
+.. _header-n507:
 
 PyGAD 2.8.1
 -----------
@@ -420,7 +420,7 @@ Release Date: 3 October 2020
    Management, Faculty of Engineering, Alexandria University,
    Egypt <https://www.linkedin.com/in/hamadakassem>`__.
 
-.. _header-n168:
+.. _header-n512:
 
 PyGAD 2.9.0 
 ------------
@@ -448,7 +448,56 @@ Release Date: 06 December 2020
    ``numpy.int64``, ``numpy.float``, ``numpy.float16``,
    ``numpy.float32``, or ``numpy.float64``.
 
-.. _header-n303:
+.. _header-n525:
+
+PyGAD 2.10.0
+------------
+
+Release Date: 03 January 2021
+
+1. Support of adaptive mutation where the mutation rate is determined by
+   the fitness value of each solution. Read the `Adaptive
+   Mutation <https://pygad.readthedocs.io/en/latest/README_pygad_ReadTheDocs.html#adaptive-mutation>`__
+   section for more details. Also, read this paper: `Libelli, S.
+   Marsili, and P. Alba. "Adaptive mutation in genetic algorithms." Soft
+   computing 4.2 (2000):
+   76-80. <https://www.researchgate.net/publication/225642916_Adaptive_mutation_in_genetic_algorithms>`__
+
+2. Before the ``run()`` method completes or exits, the fitness value of
+   the best solution in the current population is appended to the
+   ``best_solution_fitness`` list attribute. Note that the fitness value
+   of the best solution in the initial population is already saved at
+   the beginning of the list. So, the fitness value of the best solution
+   is saved before the genetic algorithm starts and after it ends.
+
+3. When the parameter ``parent_selection_type`` is set to ``sss``
+   (steady-state selection), then a warning message is printed if the
+   value of the ``keep_parents`` parameter is set to 0.
+
+4. More validations to the user input parameters.
+
+5. The default value of the ``mutation_percent_genes`` is set to the
+   string ``"default"`` rather than the integer 10. This change helps to
+   know whether the user explicitly passed a value to the
+   ``mutation_percent_genes`` parameter or it is left to its default
+   one. The ``"default"`` value is later translated into the integer 10.
+
+6. The ``mutation_percent_genes`` parameter is no longer accepting the
+   value 0. It must be ``>0`` and ``<=100``.
+
+7. The built-in ``warnings`` module is used to show warning messages
+   rather than just using the ``print()`` function.
+
+8. A new ``bool`` parameter called ``suppress_warnings`` is added to the
+   constructor of the ``pygad.GA`` class. It allows the user to control
+   whether the warning messages are printed or not. It defaults to
+   ``False`` which means the messages are printed.
+
+9. A helper method called ``adaptive_mutation_population_fitness()`` is
+   created to calculate the average fitness value used in adaptive
+   mutation to filter the solutions.
+
+.. _header-n546:
 
 PyGAD Projects at GitHub
 ========================
@@ -458,7 +507,7 @@ https://pypi.org/project/pygad. PyGAD is built out of a number of
 open-source GitHub projects. A brief note about these projects is given
 in the next subsections.
 
-.. _header-n170:
+.. _header-n548:
 
 `GeneticAlgorithmPython <https://github.com/ahmedfgad/GeneticAlgorithmPython>`__
 --------------------------------------------------------------------------------
@@ -469,7 +518,7 @@ GitHub Link: https://github.com/ahmedfgad/GeneticAlgorithmPython
 is the first project which is an open-source Python 3 project for
 implementing the genetic algorithm based on NumPy.
 
-.. _header-n173:
+.. _header-n551:
 
 `NumPyANN <https://github.com/ahmedfgad/NumPyANN>`__
 ----------------------------------------------------
@@ -483,7 +532,7 @@ neural network without using a training algorithm. Currently, it only
 supports classification and later regression will be also supported.
 Moreover, only one class is supported per sample.
 
-.. _header-n176:
+.. _header-n554:
 
 `NeuralGenetic <https://github.com/ahmedfgad/NeuralGenetic>`__
 --------------------------------------------------------------
@@ -496,7 +545,7 @@ projects
 `GeneticAlgorithmPython <https://github.com/ahmedfgad/GeneticAlgorithmPython>`__
 and `NumPyANN <https://github.com/ahmedfgad/NumPyANN>`__.
 
-.. _header-n179:
+.. _header-n557:
 
 `NumPyCNN <https://github.com/ahmedfgad/NumPyCNN>`__
 ----------------------------------------------------
@@ -508,7 +557,7 @@ convolutional neural networks using NumPy. The purpose of this project
 is to only implement the **forward pass** of a convolutional neural
 network without using a training algorithm.
 
-.. _header-n182:
+.. _header-n560:
 
 `CNNGenetic <https://github.com/ahmedfgad/CNNGenetic>`__
 --------------------------------------------------------
@@ -520,19 +569,36 @@ convolutional neural networks using the genetic algorithm. It uses the
 `GeneticAlgorithmPython <https://github.com/ahmedfgad/GeneticAlgorithmPython>`__
 project for building the genetic algorithm.
 
-.. _header-n325:
+.. _header-n563:
 
 `KerasGA <https://github.com/ahmedfgad/KerasGA>`__
 --------------------------------------------------
 
 GitHub Link: https://github.com/ahmedfgad/KerasGA
 
-`KerasGA <https://github.com/ahmedfgad/KerasGA>`__ trains Keras models
-using the genetic algorithm. It uses the
+`KerasGA <https://github.com/ahmedfgad/KerasGA>`__ trains
+`Keras <https://keras.io>`__ models using the genetic algorithm. It uses
+the
+`GeneticAlgorithmPython <https://github.com/ahmedfgad/GeneticAlgorithmPython>`__
+project for building the genetic algorithm.
+
+.. _header-n566:
+
+`TorchGA <https://github.com/ahmedfgad/TorchGA>`__
+--------------------------------------------------
+
+GitHub Link: https://github.com/ahmedfgad/TorchGA
+
+`TorchGA <https://github.com/ahmedfgad/TorchGA>`__ trains
+`PyTorch <https://pytorch.org>`__ models using the genetic algorithm. It
+uses the
 `GeneticAlgorithmPython <https://github.com/ahmedfgad/GeneticAlgorithmPython>`__
 project for building the genetic algorithm.
 
-.. _header-n185:
+`pygad.torchga <https://github.com/ahmedfgad/TorchGA>`__:
+https://github.com/ahmedfgad/TorchGA
+
+.. _header-n570:
 
 Submitting Issues
 =================
@@ -549,7 +615,7 @@ is not working properly or to ask for questions.
 If this is not a proper option for you, then check the **Contact Us**
 section for more contact details.
 
-.. _header-n189:
+.. _header-n574:
 
 Ask for Feature
 ===============
@@ -566,7 +632,7 @@ to ahmed.f.gad@gmail.com.
 
 Also check the **Contact Us** section for more contact details.
 
-.. _header-n193:
+.. _header-n578:
 
 Projects Built using PyGAD
 ==========================
@@ -585,15 +651,15 @@ Within your message, please send the following details:
 
 -  Preferably, a link that directs the readers to your project
 
-.. _header-n204:
+.. _header-n589:
 
 For More Information
 ====================
 
 There are different resources that can be used to get started with the
 genetic algorithm and building it in Python.
 
-.. _header-n206:
+.. _header-n591:
 
 Tutorial: Implementing Genetic Algorithm in Python
 --------------------------------------------------
@@ -617,7 +683,7 @@ good resource to start with coding the genetic algorithm.
 
 |image0|
 
-.. _header-n217:
+.. _header-n602:
 
 Tutorial: Introduction to Genetic Algorithm
 -------------------------------------------
@@ -636,7 +702,7 @@ which is available at these links:
 
 |image1|
 
-.. _header-n227:
+.. _header-n612:
 
 Tutorial: Build Neural Networks in Python
 -----------------------------------------
@@ -656,7 +722,7 @@ available at these links:
 
 |image2|
 
-.. _header-n237:
+.. _header-n622:
 
 Tutorial: Optimize Neural Networks with Genetic Algorithm
 ---------------------------------------------------------
@@ -676,7 +742,7 @@ available at these links:
 
 |image3|
 
-.. _header-n247:
+.. _header-n632:
 
 Tutorial: Building CNN in Python
 --------------------------------
@@ -702,7 +768,7 @@ good resource to start with coding CNNs.
 
 |image4|
 
-.. _header-n260:
+.. _header-n645:
 
 Tutorial: Derivation of CNN from FCNN
 -------------------------------------
@@ -721,7 +787,7 @@ which is available at these links:
 
 |image5|
 
-.. _header-n270:
+.. _header-n655:
 
 Book: Practical Computer Vision Applications Using Deep Learning with CNNs
 --------------------------------------------------------------------------
@@ -747,7 +813,7 @@ Find the book at these links:
 .. figure:: https://user-images.githubusercontent.com/16560492/78830077-ae7c2800-79e7-11ea-980b-53b6bd879eeb.jpg
    :alt: 
 
-.. _header-n285:
+.. _header-n670:
 
 Contact Us
 ==========