PyGAD 2.9.0

Changes in PyGAD 2.9.0 (06 December 2020):
1. The fitness values of the initial population are considered in the `best_solutions_fitness` attribute.
2. An optional parameter named `save_best_solutions` is added. It defaults to `False`. When it is `True`, then the best solution after each generation is saved into an attribute named `best_solutions`. If `False`, then no solutions are saved and the `best_solutions` attribute will be empty.
3. Scattered crossover is supported. To use it, assign the `crossover_type` parameter the value `"scattered"`.
4. NumPy arrays are now supported by the `gene_space` parameter.
5. The following parameters (`gene_type`, `crossover_probability`, `mutation_probability`, `delay_after_gen`) can be assigned to a numeric value of any of these data types: `int`, `float`, `numpy.int`, `numpy.int8`, `numpy.int16`, `numpy.int32`, `numpy.int64`, `numpy.float`, `numpy.float16`, `numpy.float32`, or `numpy.float64`.

Author: ahmedfgad

docs/source/Footer.rst

@@ -1,9 +1,9 @@
-.. _header-n298:
+.. _header-n0:
 
 Release History
 ===============
 
-.. _header-n300:
+.. _header-n2:
 
 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-n305:
+.. _header-n7:
 
 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-n316:
+.. _header-n18:
 
 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-n327:
+.. _header-n29:
 
 PyGAD 2.1.0
 -----------
@@ -97,7 +97,7 @@ Release Date: 14 May 2020
 
 2. Mutation is applied independently for the genes.
 
-.. _header-n342:
+.. _header-n44:
 
 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-n347:
+.. _header-n49:
 
 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-n360:
+.. _header-n62:
 
 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-n375:
+.. _header-n77:
 
 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-n385:
+.. _header-n87:
 
 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-n413:
+.. _header-n115:
 
 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-n422:
+.. _header-n124:
 
 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-n446:
+.. _header-n148:
 
 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-n451:
+.. _header-n153:
 
 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-n456:
+.. _header-n158:
 
 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-n597:
+.. _header-n163:
 
 PyGAD 2.8.1
 -----------
@@ -420,7 +420,35 @@ Release Date: 3 October 2020
    Management, Faculty of Engineering, Alexandria University,
    Egypt <https://www.linkedin.com/in/hamadakassem>`__.
 
-.. _header-n596:
+.. _header-n168:
+
+PyGAD 2.9.0 
+------------
+
+Release Date: 06 December 2020
+
+1. The fitness values of the initial population are considered in the
+   ``best_solutions_fitness`` attribute.
+
+2. An optional parameter named ``save_best_solutions`` is added. It
+   defaults to ``False``. When it is ``True``, then the best solution
+   after each generation is saved into an attribute named
+   ``best_solutions``. If ``False``, then no solutions are saved and the
+   ``best_solutions`` attribute will be empty.
+
+3. Scattered crossover is supported. To use it, assign the
+   ``crossover_type`` parameter the value ``"scattered"``.
+
+4. NumPy arrays are now supported by the ``gene_space`` parameter.
+
+5. The following parameters (``gene_type``, ``crossover_probability``,
+   ``mutation_probability``, ``delay_after_gen``) can be assigned to a
+   numeric value of any of these data types: ``int``, ``float``,
+   ``numpy.int``, ``numpy.int8``, ``numpy.int16``, ``numpy.int32``,
+   ``numpy.int64``, ``numpy.float``, ``numpy.float16``,
+   ``numpy.float32``, or ``numpy.float64``.
+
+.. _header-n303:
 
 PyGAD Projects at GitHub
 ========================
@@ -430,7 +458,7 @@ 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.
 
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+.. _header-n170:
 
 `GeneticAlgorithmPython <https://github.com/ahmedfgad/GeneticAlgorithmPython>`__
 --------------------------------------------------------------------------------
@@ -441,7 +469,7 @@ 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-n466:
+.. _header-n173:
 
 `NumPyANN <https://github.com/ahmedfgad/NumPyANN>`__
 ----------------------------------------------------
@@ -455,7 +483,7 @@ 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-n469:
+.. _header-n176:
 
 `NeuralGenetic <https://github.com/ahmedfgad/NeuralGenetic>`__
 --------------------------------------------------------------
@@ -468,7 +496,7 @@ projects
 `GeneticAlgorithmPython <https://github.com/ahmedfgad/GeneticAlgorithmPython>`__
 and `NumPyANN <https://github.com/ahmedfgad/NumPyANN>`__.
 
-.. _header-n472:
+.. _header-n179:
 
 `NumPyCNN <https://github.com/ahmedfgad/NumPyCNN>`__
 ----------------------------------------------------
@@ -480,7 +508,7 @@ 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-n475:
+.. _header-n182:
 
 `CNNGenetic <https://github.com/ahmedfgad/CNNGenetic>`__
 --------------------------------------------------------
@@ -492,7 +520,19 @@ convolutional neural networks using the genetic algorithm. It uses the
 `GeneticAlgorithmPython <https://github.com/ahmedfgad/GeneticAlgorithmPython>`__
 project for building the genetic algorithm.
 
-.. _header-n478:
+.. _header-n325:
+
+`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
+`GeneticAlgorithmPython <https://github.com/ahmedfgad/GeneticAlgorithmPython>`__
+project for building the genetic algorithm.
+
+.. _header-n185:
 
 Submitting Issues
 =================
@@ -509,7 +549,7 @@ 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-n482:
+.. _header-n189:
 
 Ask for Feature
 ===============
@@ -526,7 +566,7 @@ to ahmed.f.gad@gmail.com.
 
 Also check the **Contact Us** section for more contact details.
 
-.. _header-n486:
+.. _header-n193:
 
 Projects Built using PyGAD
 ==========================
@@ -545,15 +585,15 @@ Within your message, please send the following details:
 
 -  Preferably, a link that directs the readers to your project
 
-.. _header-n497:
+.. _header-n204:
 
 For More Information
 ====================
 
 There are different resources that can be used to get started with the
 genetic algorithm and building it in Python.
 
-.. _header-n499:
+.. _header-n206:
 
 Tutorial: Implementing Genetic Algorithm in Python
 --------------------------------------------------
@@ -577,7 +617,7 @@ good resource to start with coding the genetic algorithm.
 
 |image0|
 
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+.. _header-n217:
 
 Tutorial: Introduction to Genetic Algorithm
 -------------------------------------------
@@ -596,7 +636,7 @@ which is available at these links:
 
 |image1|
 
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+.. _header-n227:
 
 Tutorial: Build Neural Networks in Python
 -----------------------------------------
@@ -616,7 +656,7 @@ available at these links:
 
 |image2|
 
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+.. _header-n237:
 
 Tutorial: Optimize Neural Networks with Genetic Algorithm
 ---------------------------------------------------------
@@ -636,7 +676,7 @@ available at these links:
 
 |image3|
 
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+.. _header-n247:
 
 Tutorial: Building CNN in Python
 --------------------------------
@@ -662,7 +702,7 @@ good resource to start with coding CNNs.
 
 |image4|
 
-.. _header-n553:
+.. _header-n260:
 
 Tutorial: Derivation of CNN from FCNN
 -------------------------------------
@@ -681,7 +721,7 @@ which is available at these links:
 
 |image5|
 
-.. _header-n563:
+.. _header-n270:
 
 Book: Practical Computer Vision Applications Using Deep Learning with CNNs
 --------------------------------------------------------------------------
@@ -707,7 +747,7 @@ Find the book at these links:
 .. figure:: https://user-images.githubusercontent.com/16560492/78830077-ae7c2800-79e7-11ea-980b-53b6bd879eeb.jpg
    :alt: 
 
-.. _header-n578:
+.. _header-n285:
 
 Contact Us
 ==========