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A bug fix when save_best_solutions=True. Refer to this issue for more information: #25
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Release History | ||
=============== | ||
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.. _header-n361: | ||
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PyGAD 1.0.17 | ||
------------ | ||
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@@ -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. | ||
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.. _header-n366: | ||
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PyGAD 1.0.20 | ||
------------- | ||
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@@ -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. | ||
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.. _header-n377: | ||
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PyGAD 2.0.0 | ||
------------ | ||
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@@ -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. | ||
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.. _header-n377: | ||
.. _header-n388: | ||
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PyGAD 2.1.0 | ||
----------- | ||
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@@ -97,7 +97,7 @@ Release Date: 14 May 2020 | |
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2. Mutation is applied independently for the genes. | ||
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.. _header-n392: | ||
.. _header-n403: | ||
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PyGAD 2.2.1 | ||
----------- | ||
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@@ -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. | ||
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.. _header-n397: | ||
.. _header-n408: | ||
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PyGAD 2.2.2 | ||
----------- | ||
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@@ -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. | ||
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.. _header-n421: | ||
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PyGAD 2.3.0 | ||
----------- | ||
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@@ -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()``. | ||
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.. _header-n425: | ||
.. _header-n436: | ||
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PyGAD 2.4.0 | ||
----------- | ||
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@@ -204,7 +204,7 @@ through more generations because no further improvement is possible. | |
if ga_instance.best_solution()[1] >= 70: | ||
return "stop" | ||
.. _header-n435: | ||
.. _header-n446: | ||
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PyGAD 2.5.0 | ||
----------- | ||
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@@ -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. | ||
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.. _header-n463: | ||
.. _header-n474: | ||
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PyGAD 2.6.0 | ||
------------ | ||
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@@ -318,7 +318,7 @@ Release Date: 6 August 2020 | |
``on_fitness``, ``on_parents``, ``on_crossover``, ``on_mutation``, | ||
``on_generation``, and ``on_stop``. | ||
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.. _header-n472: | ||
.. _header-n483: | ||
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PyGAD 2.7.0 | ||
----------- | ||
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@@ -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). | ||
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.. _header-n496: | ||
.. _header-n507: | ||
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PyGAD 2.7.1 | ||
----------- | ||
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@@ -387,7 +387,7 @@ Release Date: 11 September 2020 | |
1. A bug fix when the ``problem_type`` argument is set to | ||
``regression``. | ||
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.. _header-n501: | ||
.. _header-n512: | ||
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PyGAD 2.7.2 | ||
----------- | ||
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@@ -397,7 +397,7 @@ Release Date: 14 September 2020 | |
1. Bug fix to support building and training regression neural networks | ||
with multiple outputs. | ||
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.. _header-n506: | ||
.. _header-n517: | ||
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PyGAD 2.8.0 | ||
----------- | ||
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@@ -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. | ||
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.. _header-n511: | ||
.. _header-n522: | ||
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PyGAD 2.8.1 | ||
----------- | ||
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@@ -420,7 +420,7 @@ Release Date: 3 October 2020 | |
Management, Faculty of Engineering, Alexandria University, | ||
Egypt <https://www.linkedin.com/in/hamadakassem>`__. | ||
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.. _header-n527: | ||
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PyGAD 2.9.0 | ||
------------ | ||
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@@ -448,7 +448,7 @@ Release Date: 06 December 2020 | |
``numpy.int64``, ``numpy.float``, ``numpy.float16``, | ||
``numpy.float32``, or ``numpy.float64``. | ||
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.. _header-n529: | ||
.. _header-n540: | ||
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PyGAD 2.10.0 | ||
------------ | ||
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@@ -509,7 +509,7 @@ Release Date: 03 January 2021 | |
``cal_pop_fitness()`` method is called to calculate the fitness | ||
values of the population. | ||
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.. _header-n698: | ||
.. _header-n565: | ||
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PyGAD 2.10.1 | ||
------------ | ||
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pointing about that at | ||
`GitHub <https://github.com/ahmedfgad/KerasGA/issues/1>`__. | ||
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.. _header-n554: | ||
.. _header-n721: | ||
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PyGAD 2.10.2 | ||
------------ | ||
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Release Date: 15 January 2021 | ||
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1. A bug fix when ``save_best_solutions=True``. Refer to this issue for | ||
more information: | ||
https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/25 | ||
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.. _header-n720: | ||
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PyGAD Projects at GitHub | ||
======================== | ||
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@@ -551,7 +562,7 @@ 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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`GeneticAlgorithmPython <https://github.com/ahmedfgad/GeneticAlgorithmPython>`__ | ||
-------------------------------------------------------------------------------- | ||
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@@ -562,7 +573,7 @@ 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. | ||
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.. _header-n559: | ||
.. _header-n581: | ||
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`NumPyANN <https://github.com/ahmedfgad/NumPyANN>`__ | ||
---------------------------------------------------- | ||
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supports classification and later regression will be also supported. | ||
Moreover, only one class is supported per sample. | ||
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.. _header-n562: | ||
.. _header-n584: | ||
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`NeuralGenetic <https://github.com/ahmedfgad/NeuralGenetic>`__ | ||
-------------------------------------------------------------- | ||
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`GeneticAlgorithmPython <https://github.com/ahmedfgad/GeneticAlgorithmPython>`__ | ||
and `NumPyANN <https://github.com/ahmedfgad/NumPyANN>`__. | ||
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.. _header-n565: | ||
.. _header-n587: | ||
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`NumPyCNN <https://github.com/ahmedfgad/NumPyCNN>`__ | ||
---------------------------------------------------- | ||
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@@ -601,7 +612,7 @@ 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. | ||
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.. _header-n568: | ||
.. _header-n590: | ||
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`CNNGenetic <https://github.com/ahmedfgad/CNNGenetic>`__ | ||
-------------------------------------------------------- | ||
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@@ -613,7 +624,7 @@ convolutional neural networks using the genetic algorithm. It uses the | |
`GeneticAlgorithmPython <https://github.com/ahmedfgad/GeneticAlgorithmPython>`__ | ||
project for building the genetic algorithm. | ||
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.. _header-n593: | ||
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`KerasGA <https://github.com/ahmedfgad/KerasGA>`__ | ||
-------------------------------------------------- | ||
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`GeneticAlgorithmPython <https://github.com/ahmedfgad/GeneticAlgorithmPython>`__ | ||
project for building the genetic algorithm. | ||
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.. _header-n596: | ||
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`TorchGA <https://github.com/ahmedfgad/TorchGA>`__ | ||
-------------------------------------------------- | ||
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`pygad.torchga <https://github.com/ahmedfgad/TorchGA>`__: | ||
https://github.com/ahmedfgad/TorchGA | ||
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Submitting Issues | ||
================= | ||
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@@ -659,7 +670,7 @@ 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. | ||
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Ask for Feature | ||
=============== | ||
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@@ -676,7 +687,7 @@ to [email protected]. | |
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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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@@ -695,15 +706,15 @@ Within your message, please send the following details: | |
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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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There are different resources that can be used to get started with the | ||
genetic algorithm and building it in Python. | ||
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.. _header-n599: | ||
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Tutorial: Implementing Genetic Algorithm in Python | ||
-------------------------------------------------- | ||
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|image0| | ||
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Tutorial: Introduction to Genetic Algorithm | ||
------------------------------------------- | ||
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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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|image3| | ||
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Tutorial: Building CNN in Python | ||
-------------------------------- | ||
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Tutorial: Derivation of CNN from FCNN | ||
------------------------------------- | ||
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|image5| | ||
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Book: Practical Computer Vision Applications Using Deep Learning with CNNs | ||
-------------------------------------------------------------------------- | ||
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@@ -857,7 +868,7 @@ Find the book at these links: | |
.. figure:: https://user-images.githubusercontent.com/16560492/78830077-ae7c2800-79e7-11ea-980b-53b6bd879eeb.jpg | ||
:alt: | ||
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.. _header-n678: | ||
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Contact Us | ||
========== | ||
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