PyGAD 2.11.0

Release Date: 16 February 2021

1. In the `gene_space` argument, the user can use a dictionary to specify the lower and upper limits of the gene. This dictionary must have only 2 items with keys `low` and `high` to specify the low and high limits of the gene, respectively. This way, PyGAD takes care of not exceeding the value limits of the gene. For a problem with only 2 genes, then using `gene_space=[{'low': 1, 'high': 5}, {'low': 0.2, 'high': 0.81}]` means the accepted values in the first gene start from 1 (inclusive) to 5 (exclusive) while the second one has values between 0.2 (inclusive) and 0.85 (exclusive). For more information, please check the [Limit the Gene Value Range](https://pygad.readthedocs.io/en/latest/README_pygad_ReadTheDocs.html#limit-the-gene-value-range) section of the documentation.
2. The `plot_result()` method returns the figure so that the user can save it.
3. Bug fixes in copying elements from the gene space.
4. For a gene with a set of discrete values (more than 1 value) in the `gene_space` parameter like `[0, 1]`, it was possible that the gene value may not change after mutation. That is if the current value is 0, then the randomly selected value could also be 0. Now, it is verified that the new value is changed. So, if the current value is 0, then the new value after mutation will not be 0 but 1.

Author: ahmedfgad

docs/source/Footer.rst

@@ -1,9 +1,12 @@
-.. _header-n359:
+.. _header-n0:
 
 Release History
 ===============
 
-.. _header-n361:
+.. figure:: https://user-images.githubusercontent.com/16560492/101267295-c74c0180-375f-11eb-9ad0-f8e37bd796ce.png
+   :alt: 
+
+.. _header-n3:
 
 PyGAD 1.0.17
 ------------
@@ -15,7 +18,7 @@ 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-n366:
+.. _header-n8:
 
 PyGAD 1.0.20 
 -------------
@@ -35,7 +38,7 @@ 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-n377:
+.. _header-n19:
 
 PyGAD 2.0.0 
 ------------
@@ -61,7 +64,7 @@ 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-n388:
+.. _header-n30:
 
 PyGAD 2.1.0
 -----------
@@ -97,7 +100,7 @@ Release Date: 14 May 2020
 
 2. Mutation is applied independently for the genes.
 
-.. _header-n403:
+.. _header-n45:
 
 PyGAD 2.2.1
 -----------
@@ -107,7 +110,7 @@ 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-n408:
+.. _header-n50:
 
 PyGAD 2.2.2
 -----------
@@ -141,7 +144,7 @@ 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-n421:
+.. _header-n63:
 
 PyGAD 2.3.0
 -----------
@@ -166,7 +169,7 @@ Release date: 1 June 2020
 6. The name of the ``pygad.nn.train_network()`` function is changed to
    ``pygad.nn.train()``.
 
-.. _header-n436:
+.. _header-n78:
 
 PyGAD 2.4.0
 -----------
@@ -204,7 +207,7 @@ through more generations because no further improvement is possible.
        if ga_instance.best_solution()[1] >= 70:
            return "stop"
 
-.. _header-n446:
+.. _header-n88:
 
 PyGAD 2.5.0
 -----------
@@ -300,7 +303,7 @@ 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-n474:
+.. _header-n116:
 
 PyGAD 2.6.0 
 ------------
@@ -318,7 +321,7 @@ Release Date: 6 August 2020
    ``on_fitness``, ``on_parents``, ``on_crossover``, ``on_mutation``,
    ``on_generation``, and ``on_stop``.
 
-.. _header-n483:
+.. _header-n125:
 
 PyGAD 2.7.0
 -----------
@@ -377,7 +380,7 @@ 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-n507:
+.. _header-n149:
 
 PyGAD 2.7.1
 -----------
@@ -387,7 +390,7 @@ Release Date: 11 September 2020
 1. A bug fix when the ``problem_type`` argument is set to
    ``regression``.
 
-.. _header-n512:
+.. _header-n154:
 
 PyGAD 2.7.2
 -----------
@@ -397,7 +400,7 @@ Release Date: 14 September 2020
 1. Bug fix to support building and training regression neural networks
    with multiple outputs.
 
-.. _header-n517:
+.. _header-n159:
 
 PyGAD 2.8.0
 -----------
@@ -407,7 +410,7 @@ 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-n522:
+.. _header-n164:
 
 PyGAD 2.8.1
 -----------
@@ -420,7 +423,7 @@ Release Date: 3 October 2020
    Management, Faculty of Engineering, Alexandria University,
    Egypt <https://www.linkedin.com/in/hamadakassem>`__.
 
-.. _header-n527:
+.. _header-n169:
 
 PyGAD 2.9.0 
 ------------
@@ -448,7 +451,7 @@ Release Date: 06 December 2020
    ``numpy.int64``, ``numpy.float``, ``numpy.float16``,
    ``numpy.float32``, or ``numpy.float64``.
 
-.. _header-n540:
+.. _header-n182:
 
 PyGAD 2.10.0
 ------------
@@ -509,7 +512,7 @@ Release Date: 03 January 2021
     ``cal_pop_fitness()`` method is called to calculate the fitness
     values of the population.
 
-.. _header-n565:
+.. _header-n207:
 
 PyGAD 2.10.1
 ------------
@@ -541,7 +544,7 @@ Release Date: 10 January 2021
    pointing about that at
    `GitHub <https://github.com/ahmedfgad/KerasGA/issues/1>`__.
 
-.. _header-n721:
+.. _header-n218:
 
 PyGAD 2.10.2
 ------------
@@ -552,7 +555,40 @@ Release Date: 15 January 2021
    more information:
    https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/25
 
-.. _header-n720:
+.. _header-n223:
+
+PyGAD 2.11.0
+------------
+
+Release Date: 16 February 2021
+
+1. In the ``gene_space`` argument, the user can use a dictionary to
+   specify the lower and upper limits of the gene. This dictionary must
+   have only 2 items with keys ``low`` and ``high`` to specify the low
+   and high limits of the gene, respectively. This way, PyGAD takes care
+   of not exceeding the value limits of the gene. For a problem with
+   only 2 genes, then using
+   ``gene_space=[{'low': 1, 'high': 5}, {'low': 0.2, 'high': 0.81}]``
+   means the accepted values in the first gene start from 1 (inclusive)
+   to 5 (exclusive) while the second one has values between 0.2
+   (inclusive) and 0.85 (exclusive). For more information, please check
+   the `Limit the Gene Value
+   Range <https://pygad.readthedocs.io/en/latest/README_pygad_ReadTheDocs.html#limit-the-gene-value-range>`__
+   section of the documentation.
+
+2. The ``plot_result()`` method returns the figure so that the user can
+   save it.
+
+3. Bug fixes in copying elements from the gene space.
+
+4. For a gene with a set of discrete values (more than 1 value) in the
+   ``gene_space`` parameter like ``[0, 1]``, it was possible that the
+   gene value may not change after mutation. That is if the current
+   value is 0, then the randomly selected value could also be 0. Now, it
+   is verified that the new value is changed. So, if the current value
+   is 0, then the new value after mutation will not be 0 but 1.
+
+.. _header-n234:
 
 PyGAD Projects at GitHub
 ========================
@@ -562,7 +598,7 @@ 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-n578:
+.. _header-n236:
 
 `GeneticAlgorithmPython <https://github.com/ahmedfgad/GeneticAlgorithmPython>`__
 --------------------------------------------------------------------------------
@@ -573,7 +609,7 @@ 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-n581:
+.. _header-n239:
 
 `NumPyANN <https://github.com/ahmedfgad/NumPyANN>`__
 ----------------------------------------------------
@@ -587,7 +623,7 @@ 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-n584:
+.. _header-n242:
 
 `NeuralGenetic <https://github.com/ahmedfgad/NeuralGenetic>`__
 --------------------------------------------------------------
@@ -600,7 +636,7 @@ projects
 `GeneticAlgorithmPython <https://github.com/ahmedfgad/GeneticAlgorithmPython>`__
 and `NumPyANN <https://github.com/ahmedfgad/NumPyANN>`__.
 
-.. _header-n587:
+.. _header-n245:
 
 `NumPyCNN <https://github.com/ahmedfgad/NumPyCNN>`__
 ----------------------------------------------------
@@ -612,7 +648,7 @@ 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-n590:
+.. _header-n248:
 
 `CNNGenetic <https://github.com/ahmedfgad/CNNGenetic>`__
 --------------------------------------------------------
@@ -624,7 +660,7 @@ convolutional neural networks using the genetic algorithm. It uses the
 `GeneticAlgorithmPython <https://github.com/ahmedfgad/GeneticAlgorithmPython>`__
 project for building the genetic algorithm.
 
-.. _header-n593:
+.. _header-n251:
 
 `KerasGA <https://github.com/ahmedfgad/KerasGA>`__
 --------------------------------------------------
@@ -637,7 +673,7 @@ the
 `GeneticAlgorithmPython <https://github.com/ahmedfgad/GeneticAlgorithmPython>`__
 project for building the genetic algorithm.
 
-.. _header-n596:
+.. _header-n254:
 
 `TorchGA <https://github.com/ahmedfgad/TorchGA>`__
 --------------------------------------------------
@@ -653,7 +689,7 @@ project for building the genetic algorithm.
 `pygad.torchga <https://github.com/ahmedfgad/TorchGA>`__:
 https://github.com/ahmedfgad/TorchGA
 
-.. _header-n600:
+.. _header-n258:
 
 Submitting Issues
 =================
@@ -670,7 +706,7 @@ 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-n604:
+.. _header-n262:
 
 Ask for Feature
 ===============
@@ -687,7 +723,7 @@ to ahmed.f.gad@gmail.com.
 
 Also check the **Contact Us** section for more contact details.
 
-.. _header-n608:
+.. _header-n266:
 
 Projects Built using PyGAD
 ==========================
@@ -706,15 +742,15 @@ Within your message, please send the following details:
 
 -  Preferably, a link that directs the readers to your project
 
-.. _header-n619:
+.. _header-n277:
 
 For More Information
 ====================
 
 There are different resources that can be used to get started with the
 genetic algorithm and building it in Python.
 
-.. _header-n621:
+.. _header-n279:
 
 Tutorial: Implementing Genetic Algorithm in Python
 --------------------------------------------------
@@ -738,7 +774,7 @@ good resource to start with coding the genetic algorithm.
 
 |image0|
 
-.. _header-n632:
+.. _header-n290:
 
 Tutorial: Introduction to Genetic Algorithm
 -------------------------------------------
@@ -757,7 +793,7 @@ which is available at these links:
 
 |image1|
 
-.. _header-n642:
+.. _header-n300:
 
 Tutorial: Build Neural Networks in Python
 -----------------------------------------
@@ -777,7 +813,7 @@ available at these links:
 
 |image2|
 
-.. _header-n652:
+.. _header-n310:
 
 Tutorial: Optimize Neural Networks with Genetic Algorithm
 ---------------------------------------------------------
@@ -797,7 +833,7 @@ available at these links:
 
 |image3|
 
-.. _header-n662:
+.. _header-n320:
 
 Tutorial: Building CNN in Python
 --------------------------------
@@ -823,7 +859,7 @@ good resource to start with coding CNNs.
 
 |image4|
 
-.. _header-n675:
+.. _header-n333:
 
 Tutorial: Derivation of CNN from FCNN
 -------------------------------------
@@ -842,7 +878,7 @@ which is available at these links:
 
 |image5|
 
-.. _header-n685:
+.. _header-n343:
 
 Book: Practical Computer Vision Applications Using Deep Learning with CNNs
 --------------------------------------------------------------------------
@@ -868,7 +904,7 @@ Find the book at these links:
 .. figure:: https://user-images.githubusercontent.com/16560492/78830077-ae7c2800-79e7-11ea-980b-53b6bd879eeb.jpg
    :alt: 
 
-.. _header-n700:
+.. _header-n358:
 
 Contact Us
 ==========
@@ -889,6 +925,9 @@ Contact Us
 
 -  `GitHub <https://github.com/ahmedfgad>`__
 
+.. figure:: https://user-images.githubusercontent.com/16560492/101267295-c74c0180-375f-11eb-9ad0-f8e37bd796ce.png
+   :alt: 
+
 .. |image0| image:: https://user-images.githubusercontent.com/16560492/78830052-a3c19300-79e7-11ea-8b9b-4b343ea4049c.png
    :target: https://www.linkedin.com/pulse/genetic-algorithm-implementation-python-ahmed-gad
 .. |image1| image:: https://user-images.githubusercontent.com/16560492/82078259-26252d00-96e1-11ea-9a02-52a99e1054b9.jpg