Merge github-actions: host documentation images and add lifecycle SVG

Author: ahmedfgad

README.md

@@ -16,7 +16,7 @@ Read the [PyGAD documentation](https://pygad.readthedocs.io/en/latest).
 https://anaconda.org/conda-forge/PyGAD) [![PyPI version](https://badge.fury.io/py/pygad.svg)](https://badge.fury.io/py/pygad)![Docs](https://readthedocs.org/projects/pygad/badge)[![PyGAD PyTest Matrix](https://github.com/ahmedfgad/GeneticAlgorithmPython/actions/workflows/main.yml/badge.svg)](https://github.com/ahmedfgad/GeneticAlgorithmPython/actions/workflows/main.yml) [![Release](https://github.com/ahmedfgad/GeneticAlgorithmPython/actions/workflows/release.yml/badge.svg)](https://github.com/ahmedfgad/GeneticAlgorithmPython/actions/workflows/release.yml) [![Scorecard supply-chain security](https://github.com/ahmedfgad/GeneticAlgorithmPython/actions/workflows/scorecard.yml/badge.svg)](https://github.com/ahmedfgad/GeneticAlgorithmPython/actions/workflows/scorecard.yml) [![License](https://img.shields.io/badge/License-BSD_3--Clause-blue.svg)](https://opensource.org/licenses/BSD-3-Clause) [![REUSE](https://api.reuse.software/badge/github.com/ahmedfgad/GeneticAlgorithmPython)](https://api.reuse.software/info/github.com/ahmedfgad/GeneticAlgorithmPython) [![Stack Overflow](https://img.shields.io/badge/stackoverflow-Ask%20questions-blue.svg)](
 https://stackoverflow.com/questions/tagged/pygad) [![OpenSSF Scorecard](https://api.securityscorecards.dev/projects/github.com/ahmedfgad/GeneticAlgorithmPython/badge)](https://securityscorecards.dev/viewer/?uri=github.com/ahmedfgad/GeneticAlgorithmPython) [![DOI](https://zenodo.org/badge/DOI/10.1007/s11042-023-17167-y.svg)](https://doi.org/10.1007/s11042-023-17167-y)
 
-![PYGAD-LOGO](https://user-images.githubusercontent.com/16560492/101267295-c74c0180-375f-11eb-9ad0-f8e37bd796ce.png)
+![PYGAD-LOGO](https://github.com/ahmedfgad/GeneticAlgorithmPython/raw/master/docs/source/images/101267295-c74c0180-375f-11eb-9ad0-f8e37bd796ce.png)
 
 [PyGAD](https://pypi.org/project/pygad) supports different types of crossover, mutation, and parent selection. It lets you optimize many types of problems with the genetic algorithm by writing your own fitness function.
 
@@ -68,7 +68,7 @@ Please check the **Contact Us** section for more contact details.
 
 The following figure shows the main stages in the life cycle of a `pygad.GA` instance. PyGAD stops when all generations are completed or when the function passed to the `on_generation` parameter returns the string `stop`.
 
-![PyGAD Lifecycle](https://user-images.githubusercontent.com/16560492/220486073-c5b6089d-81e4-44d9-a53c-385f479a7273.jpg)
+![PyGAD Lifecycle](https://github.com/ahmedfgad/GeneticAlgorithmPython/raw/master/docs/source/images/pygad_lifecycle.png)
 
 The following code implements all the callback functions to trace the execution of the genetic algorithm. Each callback function prints its name.
 
@@ -242,7 +242,7 @@ To start with coding the genetic algorithm, you can check the tutorial titled [*
 
 [This tutorial](https://www.linkedin.com/pulse/genetic-algorithm-implementation-python-ahmed-gad) is based on an earlier version of the project, but it is still a good resource to start coding the genetic algorithm.
 
-[![Genetic Algorithm Implementation in Python](https://user-images.githubusercontent.com/16560492/78830052-a3c19300-79e7-11ea-8b9b-4b343ea4049c.png)](https://www.linkedin.com/pulse/genetic-algorithm-implementation-python-ahmed-gad)
+[![Genetic Algorithm Implementation in Python](https://github.com/ahmedfgad/GeneticAlgorithmPython/raw/master/docs/source/images/78830052-a3c19300-79e7-11ea-8b9b-4b343ea4049c.png)](https://www.linkedin.com/pulse/genetic-algorithm-implementation-python-ahmed-gad)
 
 ## Tutorial: Introduction to Genetic Algorithm
 
@@ -252,7 +252,7 @@ Get started with the genetic algorithm by reading the tutorial titled [**Introdu
 * [Towards Data Science](https://towardsdatascience.com/introduction-to-optimization-with-genetic-algorithm-2f5001d9964b)
 * [KDnuggets](https://www.kdnuggets.com/2018/03/introduction-optimization-with-genetic-algorithm.html)
 
-[![Introduction to Genetic Algorithm](https://user-images.githubusercontent.com/16560492/82078259-26252d00-96e1-11ea-9a02-52a99e1054b9.jpg)](https://www.linkedin.com/pulse/introduction-optimization-genetic-algorithm-ahmed-gad)
+[![Introduction to Genetic Algorithm](https://github.com/ahmedfgad/GeneticAlgorithmPython/raw/master/docs/source/images/82078259-26252d00-96e1-11ea-9a02-52a99e1054b9.jpg)](https://www.linkedin.com/pulse/introduction-optimization-genetic-algorithm-ahmed-gad)
 
 ## Tutorial: Optimize Neural Networks with Genetic Algorithm
 
@@ -262,7 +262,7 @@ Read about training neural networks using the genetic algorithm through the tuto
 - [Towards Data Science](https://towardsdatascience.com/artificial-neural-networks-optimization-using-genetic-algorithm-with-python-1fe8ed17733e)
 - [KDnuggets](https://www.kdnuggets.com/2019/03/artificial-neural-networks-optimization-genetic-algorithm-python.html)
 
-[![Training Neural Networks using Genetic Algorithm Python](https://user-images.githubusercontent.com/16560492/82078300-376e3980-96e1-11ea-821c-aa6b8ceb44d4.jpg)](https://www.linkedin.com/pulse/artificial-neural-networks-optimization-using-genetic-ahmed-gad)
+[![Training Neural Networks using Genetic Algorithm Python](https://github.com/ahmedfgad/GeneticAlgorithmPython/raw/master/docs/source/images/82078300-376e3980-96e1-11ea-821c-aa6b8ceb44d4.jpg)](https://www.linkedin.com/pulse/artificial-neural-networks-optimization-using-genetic-ahmed-gad)
 
 ## Book: Practical Computer Vision Applications Using Deep Learning with CNNs
 
@@ -276,7 +276,7 @@ Find the book at these links:
 - [O'Reilly](https://www.oreilly.com/library/view/practical-computer-vision/9781484241677)
 - [Google Books](https://books.google.com.eg/books?id=xLd9DwAAQBAJ)
 
-![Fig04](https://user-images.githubusercontent.com/16560492/78830077-ae7c2800-79e7-11ea-980b-53b6bd879eeb.jpg)
+![Fig04](https://github.com/ahmedfgad/GeneticAlgorithmPython/raw/master/docs/source/images/78830077-ae7c2800-79e7-11ea-980b-53b6bd879eeb.jpg)
 
 # Citing PyGAD - Bibtex Formatted Citation
 

docs/source/adaptive_mutation.md

@@ -23,7 +23,7 @@ In PyGAD, if `f=f_avg`, then the solution is regarded as high quality.
 
 The next figure summarizes the previous steps.
 
-![Adaptive-Mutation](https://user-images.githubusercontent.com/16560492/103468973-e3c26600-4d2c-11eb-8af3-b3bb39b50540.jpg)
+![Adaptive-Mutation](images/103468973-e3c26600-4d2c-11eb-8af3-b3bb39b50540.jpg)
 
 This strategy is applied in PyGAD. 
 

docs/source/gacnn.md

@@ -296,7 +296,7 @@ After the `run()` method completes, the `plot_fitness()` method can be called to
 ga_instance.plot_fitness()
 ```
 
-![GACNN_Fitness](https://user-images.githubusercontent.com/16560492/83429675-ab744580-a434-11ea-8f21-9d3804b50d15.png)
+![GACNN_Fitness](images/83429675-ab744580-a434-11ea-8f21-9d3804b50d15.png)
 
 ### Information about the Best Solution
 

docs/source/gann.md

@@ -310,7 +310,7 @@ After the `run()` method completes, the `plot_fitness()` method can be called to
 ga_instance.plot_fitness()
 ```
 
-![XOR_Fitness](https://user-images.githubusercontent.com/16560492/82078638-c11e0700-96e1-11ea-8aa9-c36761c5e9c7.png)
+![XOR_Fitness](images/82078638-c11e0700-96e1-11ea-8aa9-c36761c5e9c7.png)
 
 By running the code again, a different initial population is created, so a classification accuracy of 100 can be reached using fewer generations. On the other hand, a different initial population might cause 100% accuracy to be reached using more generations or not reached at all.
 

docs/source/gann_image_classification.md

@@ -143,4 +143,4 @@ Classification accuracy : 99.94903160040775.
 
 The next figure shows how fitness value evolves by generation.
 
-![Training Neural Networks using Genetic Algorithm](https://user-images.githubusercontent.com/16560492/82152993-21898180-9865-11ea-8387-b995f88b83f7.png)
+![Training Neural Networks using Genetic Algorithm](images/82152993-21898180-9865-11ea-8387-b995f88b83f7.png)

docs/source/gann_regression_1.md

@@ -150,4 +150,4 @@ print(f"Absolute error : {abs_error}.")
 
 The next figure shows how the fitness value changes for the generations used.
 
-![example_regression](https://user-images.githubusercontent.com/16560492/92948154-3cf24b00-f459-11ea-94ea-952b66ab2145.png)
+![example_regression](images/92948154-3cf24b00-f459-11ea-94ea-952b66ab2145.png)

docs/source/gann_regression_2.md

@@ -145,4 +145,4 @@ print(f"Absolute error : {abs_error}.")
 
 The next figure shows how the fitness value changes for the 500 generations used.
 
-![example_regression_fish](https://user-images.githubusercontent.com/16560492/92948486-bbe78380-f459-11ea-9e31-0d4c7269d606.png)
+![example_regression_fish](images/92948486-bbe78380-f459-11ea-9e31-0d4c7269d606.png)

docs/source/generations.md

@@ -137,7 +137,7 @@ ga_instance.run()
 
 
 
-![elitism_kills_evolution](https://user-images.githubusercontent.com/16560492/189273225-67ffad41-97ab-45e1-9324-429705e17b20.png)
+![elitism_kills_evolution](images/189273225-67ffad41-97ab-45e1-9324-429705e17b20.png)
 
 ### How the Number of Offspring Is Decided
 

docs/source/help_languages.md

@@ -8,7 +8,7 @@ Bien que la manière standard d'entraîner les réseaux de neurones soit la desc
 
 Utiliser un algorithme génétique pour former un réseau de neurones simple pour résoudre le OpenAI CartPole Jeu. Dans cet article, nous allons former un simple réseau de neurones pour résoudre le OpenAI CartPole . J'utiliserai PyTorch et PyGAD .
 
-[![Cómo los algoritmos genéticos pueden competir con el descenso de gradiente y el backprop](https://user-images.githubusercontent.com/16560492/111009275-3178d180-8361-11eb-9e86-7fb1519acde7.png)](https://www.hebergementwebs.com/nouvelles/comment-les-algorithmes-genetiques-peuvent-rivaliser-avec-la-descente-de-gradient-et-le-backprop)
+[![Cómo los algoritmos genéticos pueden competir con el descenso de gradiente y el backprop](images/111009275-3178d180-8361-11eb-9e86-7fb1519acde7.png)](https://www.hebergementwebs.com/nouvelles/comment-les-algorithmes-genetiques-peuvent-rivaliser-avec-la-descente-de-gradient-et-le-backprop)
 
 ## Spanish
 
@@ -18,13 +18,13 @@ Aunque la forma estandar de entrenar redes neuronales es el descenso de gradient
 
 Usa un algoritmo genetico para entrenar una red neuronal simple para resolver el Juego OpenAI CartPole. En este articulo, entrenaremos una red neuronal simple para resolver el OpenAI CartPole . Usare PyTorch y PyGAD .
 
-[![Cómo los algoritmos genéticos pueden competir con el descenso de gradiente y el backprop](https://user-images.githubusercontent.com/16560492/111009257-232ab580-8361-11eb-99a5-7226efbc3065.png)](https://www.hebergementwebs.com/noticias/como-los-algoritmos-geneticos-pueden-competir-con-el-descenso-de-gradiente-y-el-backprop)
+[![Cómo los algoritmos genéticos pueden competir con el descenso de gradiente y el backprop](images/111009257-232ab580-8361-11eb-99a5-7226efbc3065.png)](https://www.hebergementwebs.com/noticias/como-los-algoritmos-geneticos-pueden-competir-con-el-descenso-de-gradiente-y-el-backprop)
 
 ## Korean
 
 ### [[PyGAD] Python 에서 Genetic Algorithm 을 사용해보기](https://data-newbie.tistory.com/m/685)
 
-[![Korean-1](https://user-images.githubusercontent.com/16560492/108586306-85bd0280-731b-11eb-874c-7ac4ce1326cd.jpg)](https://data-newbie.tistory.com/m/685)
+[![Korean-1](images/108586306-85bd0280-731b-11eb-874c-7ac4ce1326cd.jpg)](https://data-newbie.tistory.com/m/685)
 
 파이썬에서 genetic algorithm을 사용하는 패키지들을 다 사용해보진 않았지만, 확장성이 있어보이고, 시도할 일이 있어서 살펴봤다.
 
@@ -50,7 +50,7 @@ En son PyGAD sürümü 2.8.0 (20 Eylül 2020'de piyasaya sürüldü), Keras mode
 
 Bu öğreticide, PyGAD kullanılarak Keras modellerinin nasıl eğitileceği anlatılmaktadır. Tartışma, Sıralı Modeli veya İşlevsel API’yi kullanarak Keras modellerini oluşturmayı, Keras model parametrelerinin ilk popülasyonunu oluşturmayı, uygun bir uygunluk işlevi oluşturmayı ve daha fazlasını içerir.
 
-[![national-cancer-institute-zz_3tCcrk7o-unsplash](https://user-images.githubusercontent.com/16560492/108586601-85be0200-731d-11eb-98a4-161c75a1f099.jpg)](https://erencan34.medium.com/pygad-ile-genetik-algoritmay%C4%B1-kullanarak-keras-modelleri-nas%C4%B1l-e%C4%9Fitilir-cf92639a478c)
+[![national-cancer-institute-zz_3tCcrk7o-unsplash](images/108586601-85be0200-731d-11eb-98a4-161c75a1f099.jpg)](https://erencan34.medium.com/pygad-ile-genetik-algoritmay%C4%B1-kullanarak-keras-modelleri-nas%C4%B1l-e%C4%9Fitilir-cf92639a478c)
 
 ## Hungarian
 
@@ -62,7 +62,7 @@ A hálózatok tenyésztéséhez a [PyGAD](https://pygad.readthedocs.io/en/latest
 
 Maga a PyGAD egy teljesen általános genetikus algoritmusok futtatására képes rendszer. Ennek a kiterjesztése a KerasGA, ami az általános motor Tensorflow (Keras) neurális hálókon történő futtatását segíti. A 47. sorban létrehozott KerasGA objektum ennek a kiterjesztésnek a része és arra szolgál, hogy a paraméterként átadott modellből a második paraméterben megadott számosságú populációt hozzon létre. Mivel a hálózatunk 386 állítható paraméterrel rendelkezik, ezért a DNS-ünk itt 386 elemből fog állni. A populáció mérete 10 egyed, így a kezdő populációnk egy 10x386 elemű mátrix lesz. Ezt adjuk át az 51. sorban az initial_population paraméterben.
 
-[![](https://user-images.githubusercontent.com/16560492/101267295-c74c0180-375f-11eb-9ad0-f8e37bd796ce.png)](https://thebojda.medium.com/tensorflow-alapoz%C3%B3-10-24f7767d4a2c)
+[![](images/101267295-c74c0180-375f-11eb-9ad0-f8e37bd796ce.png)](https://thebojda.medium.com/tensorflow-alapoz%C3%B3-10-24f7767d4a2c)
 
 ## Russian
 
@@ -78,4 +78,4 @@ PyGAD — это библиотека для имплементации гене
 
 PyGAD разрабатывали на Python 3.7.3. Зависимости включают в себя NumPy для создания и манипуляции массивами и Matplotlib для визуализации. Один из изкейсов использования инструмента — оптимизация весов, которые удовлетворяют заданной функции.
 
-[![](https://user-images.githubusercontent.com/16560492/101267295-c74c0180-375f-11eb-9ad0-f8e37bd796ce.png)](https://neurohive.io/ru/frameworki/pygad-biblioteka-dlya-implementacii-geneticheskogo-algoritma)
+[![](images/101267295-c74c0180-375f-11eb-9ad0-f8e37bd796ce.png)](https://neurohive.io/ru/frameworki/pygad-biblioteka-dlya-implementacii-geneticheskogo-algoritma)

docs/source/help_support.md

@@ -59,6 +59,6 @@ https://blog.csdn.net/sinat_38079265/article/details/108449614
 * [TowardsDataScience](https://towardsdatascience.com/@ahmedfgad)
 * [GitHub](https://github.com/ahmedfgad)
 
-![PYGAD-LOGO](https://user-images.githubusercontent.com/16560492/101267295-c74c0180-375f-11eb-9ad0-f8e37bd796ce.png)
+![PYGAD-LOGO](images/101267295-c74c0180-375f-11eb-9ad0-f8e37bd796ce.png)
 
 Thank you for using [PyGAD](https://github.com/ahmedfgad/GeneticAlgorithmPython) :)