{"id":13738581,"url":"https://github.com/ahmedfgad/NeuralGenetic","last_synced_at":"2025-05-08T16:34:44.496Z","repository":{"id":50064157,"uuid":"167374649","full_name":"ahmedfgad/NeuralGenetic","owner":"ahmedfgad","description":"Building and training artificial neural networks (regression or classification) using the genetic algorithm.","archived":false,"fork":false,"pushed_at":"2024-02-04T22:40:43.000Z","size":1341,"stargazers_count":246,"open_issues_count":3,"forks_count":90,"subscribers_count":10,"default_branch":"master","last_synced_at":"2025-04-07T12:06:23.344Z","etag":null,"topics":["deep-learning","genetic-algorithm","neural-networks","optimization","pygad","python"],"latest_commit_sha":null,"homepage":"https://pygad.readthedocs.io","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/ahmedfgad.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":".github/FUNDING.yml","license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null},"funding":{"github":null,"open_collective":"pygad","ko_fi":null,"tidelift":null,"community_bridge":null,"liberapay":null,"issuehunt":null,"otechie":null,"custom":["https://donate.stripe.com/eVa5kO866elKgM0144","http://paypal.me/ahmedfgad"]}},"created_at":"2019-01-24T13:47:06.000Z","updated_at":"2025-02-21T12:33:18.000Z","dependencies_parsed_at":"2024-11-15T07:31:38.790Z","dependency_job_id":"bf10d154-d515-4365-81ad-b5e299bf57c2","html_url":"https://github.com/ahmedfgad/NeuralGenetic","commit_stats":{"total_commits":51,"total_committers":1,"mean_commits":51.0,"dds":0.0,"last_synced_commit":"54be7a6a860593eb3853d6a3deb207eaf35eea9f"},"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ahmedfgad%2FNeuralGenetic","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ahmedfgad%2FNeuralGenetic/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ahmedfgad%2FNeuralGenetic/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ahmedfgad%2FNeuralGenetic/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/ahmedfgad","download_url":"https://codeload.github.com/ahmedfgad/NeuralGenetic/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":253105681,"owners_count":21855077,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["deep-learning","genetic-algorithm","neural-networks","optimization","pygad","python"],"created_at":"2024-08-03T03:02:27.838Z","updated_at":"2025-05-08T16:34:43.490Z","avatar_url":"https://github.com/ahmedfgad.png","language":"Python","funding_links":["https://opencollective.com/pygad","https://donate.stripe.com/eVa5kO866elKgM0144","http://paypal.me/ahmedfgad","https://paypal.me/ahmedfgad"],"categories":["Python"],"sub_categories":[],"readme":"# NeuralGenetic: Training Neural Networks using the Genetic Algorithm\n\n[NeuralGenetic](https://github.com/ahmedfgad/NeuralGenetic) is a Python project for training neural networks using the genetic algorithm. \n\n[NeuralGenetic](https://github.com/ahmedfgad/NeuralGenetic) is part of the [PyGAD](https://pypi.org/project/pygad) library which is an open-source Python 3 library for implementing the genetic algorithm and optimizing machine learning algorithms. Both regression and classification neural networks are supported starting from PyGAD 2.7.0.\n\nCheck documentation of the [NeuralGenetic](https://github.com/ahmedfgad/NeuralGenetic) project in the PyGAD's documentation: https://pygad.readthedocs.io/en/latest/gann.html\n\nThe library is under active development and more features are added regularly. If you want a feature to be supported, please check the **Contact Us** section to send a request.\n\n# Donation\n\n- [Credit/Debit Card](https://donate.stripe.com/eVa5kO866elKgM0144): https://donate.stripe.com/eVa5kO866elKgM0144\n- [Open Collective](https://opencollective.com/pygad): [opencollective.com/pygad](https://opencollective.com/pygad)\n- PayPal: Use either this link: [paypal.me/ahmedfgad](https://paypal.me/ahmedfgad) or the e-mail address ahmed.f.gad@gmail.com\n- Interac e-Transfer: Use e-mail address ahmed.f.gad@gmail.com\n\n# Tutorial Project\n\n**IMPORTANT** If you are coming for the code of the tutorial titled [**Artificial Neural Networks Optimization using Genetic Algorithm with Python**](https://www.linkedin.com/pulse/artificial-neural-networks-optimization-using-genetic-ahmed-gad), then it has been moved to the [Tutorial Project](https://github.com/ahmedfgad/NeuralGenetic/tree/master/Tutorial Project) directory on 15 May 2020.\n\n# Installation\n\nTo install [PyGAD](https://pypi.org/project/pygad), simply use pip to download and install the library from [PyPI](https://pypi.org/project/pygad) (Python Package Index). The library is at PyPI at this page https://pypi.org/project/pygad.\n\nInstall PyGAD with the following command:\n\n```python\npip install pygad\n```\n\nTo get started with PyGAD, please read the documentation at [Read The Docs](https://pygad.readthedocs.io/) https://pygad.readthedocs.io.\n\n# PyGAD Source Code\n\nThe source code of the PyGAD' modules is found in the following GitHub projects:\n\n- [pygad](https://github.com/ahmedfgad/GeneticAlgorithmPython): (https://github.com/ahmedfgad/GeneticAlgorithmPython)\n- [pygad.nn](https://github.com/ahmedfgad/NumPyANN): https://github.com/ahmedfgad/NumPyANN\n- [pygad.gann](https://github.com/ahmedfgad/NeuralGenetic): https://github.com/ahmedfgad/NeuralGenetic\n- [pygad.cnn](https://github.com/ahmedfgad/NumPyCNN): https://github.com/ahmedfgad/NumPyCNN\n- [pygad.gacnn](https://github.com/ahmedfgad/CNNGenetic): https://github.com/ahmedfgad/CNNGenetic\n- [pygad.kerasga](https://github.com/ahmedfgad/KerasGA): https://github.com/ahmedfgad/KerasGA\n- [pygad.torchga](https://github.com/ahmedfgad/TorchGA): https://github.com/ahmedfgad/TorchGA\n\nThe documentation of PyGAD is available at [Read The Docs](https://pygad.readthedocs.io/) https://pygad.readthedocs.io.\n\n# PyGAD Documentation\n\nThe documentation of the PyGAD library is available at [Read The Docs](https://pygad.readthedocs.io) at this link: https://pygad.readthedocs.io. It discusses the modules supported by PyGAD, all its classes, methods, attribute, and functions. For each module, a number of examples are given.\n\nIf there is an issue using PyGAD, feel free to post at issue in this [GitHub repository](https://github.com/ahmedfgad/GeneticAlgorithmPython) https://github.com/ahmedfgad/GeneticAlgorithmPython or by sending an e-mail to ahmed.f.gad@gmail.com. \n\nIf you built a project that uses PyGAD, then please drop an e-mail to ahmed.f.gad@gmail.com with the following information so that your project is included in the documentation.\n\n- Project title\n- Brief description\n- Preferably, a link that directs the readers to your project\n\nPlease check the **Contact Us** section for more contact details.\n\n# Life Cycle of PyGAD\n\nThe next figure lists the different stages in the lifecycle of an instance of the `pygad.GA` class. Note that PyGAD stops when either all generations are completed or when the function passed to the `on_generation` parameter returns the string `stop`.\n\n![PyGAD Lifecycle](https://user-images.githubusercontent.com/16560492/89446279-9c6f8380-d754-11ea-83fd-a60ea2f53b85.jpg)\n\nThe next code implements all the callback functions to trace the execution of the genetic algorithm. Each callback function prints its name.\n\n```python\nimport pygad\nimport numpy\n\nfunction_inputs = [4,-2,3.5,5,-11,-4.7]\ndesired_output = 44\n\ndef fitness_func(ga_instance, solution, solution_idx):\n    output = numpy.sum(solution*function_inputs)\n    fitness = 1.0 / (numpy.abs(output - desired_output) + 0.000001)\n    return fitness\n\nfitness_function = fitness_func\n\ndef on_start(ga_instance):\n    print(\"on_start()\")\n\ndef on_fitness(ga_instance, population_fitness):\n    print(\"on_fitness()\")\n\ndef on_parents(ga_instance, selected_parents):\n    print(\"on_parents()\")\n\ndef on_crossover(ga_instance, offspring_crossover):\n    print(\"on_crossover()\")\n\ndef on_mutation(ga_instance, offspring_mutation):\n    print(\"on_mutation()\")\n\ndef on_generation(ga_instance):\n    print(\"on_generation()\")\n\ndef on_stop(ga_instance, last_population_fitness):\n    print(\"on_stop()\")\n\nga_instance = pygad.GA(num_generations=3,\n                       num_parents_mating=5,\n                       fitness_func=fitness_function,\n                       sol_per_pop=10,\n                       num_genes=len(function_inputs),\n                       on_start=on_start,\n                       on_fitness=on_fitness,\n                       on_parents=on_parents,\n                       on_crossover=on_crossover,\n                       on_mutation=on_mutation,\n                       on_generation=on_generation,\n                       on_stop=on_stop)\n\nga_instance.run()\n```\n\nBased on the used 3 generations as assigned to the `num_generations` argument, here is the output.\n\n```\non_start()\n\non_fitness()\non_parents()\non_crossover()\non_mutation()\non_generation()\n\non_fitness()\non_parents()\non_crossover()\non_mutation()\non_generation()\n\non_fitness()\non_parents()\non_crossover()\non_mutation()\non_generation()\n\non_stop()\n```\n\n# Example\n\nCheck the [PyGAD's documentation](https://pygad.readthedocs.io/en/latest/gann.html) for information about the implementation of this example.\n\n```python\nimport numpy\nimport pygad\nimport pygad.nn\nimport pygad.gann\n\ndef fitness_func(ga_instance, solution, sol_idx):\n    global GANN_instance, data_inputs, data_outputs\n\n    predictions = pygad.nn.predict(last_layer=GANN_instance.population_networks[sol_idx],\n                                   data_inputs=data_inputs)\n    correct_predictions = numpy.where(predictions == data_outputs)[0].size\n    solution_fitness = (correct_predictions/data_outputs.size)*100\n\n    return solution_fitness\n\ndef callback_generation(ga_instance):\n    global GANN_instance, last_fitness\n\n    population_matrices = pygad.gann.population_as_matrices(population_networks=GANN_instance.population_networks, \n                                                            population_vectors=ga_instance.population)\n\n    GANN_instance.update_population_trained_weights(population_trained_weights=population_matrices)\n\n    print(\"Generation = {generation}\".format(generation=ga_instance.generations_completed))\n    print(\"Fitness    = {fitness}\".format(fitness=ga_instance.best_solution()[1]))\n    print(\"Change     = {change}\".format(change=ga_instance.best_solution()[1] - last_fitness))\n\n    last_fitness = ga_instance.best_solution()[1].copy()\n\n# Holds the fitness value of the previous generation.\nlast_fitness = 0\n\n# Preparing the NumPy array of the inputs.\ndata_inputs = numpy.array([[1, 1],\n                           [1, 0],\n                           [0, 1],\n                           [0, 0]])\n\n# Preparing the NumPy array of the outputs.\ndata_outputs = numpy.array([0, \n                            1, \n                            1, \n                            0])\n\n# The length of the input vector for each sample (i.e. number of neurons in the input layer).\nnum_inputs = data_inputs.shape[1]\n# The number of neurons in the output layer (i.e. number of classes).\nnum_classes = 2\n\n# Creating an initial population of neural networks. The return of the initial_population() function holds references to the networks, not their weights. Using such references, the weights of all networks can be fetched.\nnum_solutions = 6 # A solution or a network can be used interchangeably.\nGANN_instance = pygad.gann.GANN(num_solutions=num_solutions,\n                                num_neurons_input=num_inputs,\n                                num_neurons_hidden_layers=[2],\n                                num_neurons_output=num_classes,\n                                hidden_activations=[\"relu\"],\n                                output_activation=\"softmax\")\n\n# population does not hold the numerical weights of the network instead it holds a list of references to each last layer of each network (i.e. solution) in the population. A solution or a network can be used interchangeably.\n# If there is a population with 3 solutions (i.e. networks), then the population is a list with 3 elements. Each element is a reference to the last layer of each network. Using such a reference, all details of the network can be accessed.\npopulation_vectors = pygad.gann.population_as_vectors(population_networks=GANN_instance.population_networks)\n\n# To prepare the initial population, there are 2 ways:\n# 1) Prepare it yourself and pass it to the initial_population parameter. This way is useful when the user wants to start the genetic algorithm with a custom initial population.\n# 2) Assign valid integer values to the sol_per_pop and num_genes parameters. If the initial_population parameter exists, then the sol_per_pop and num_genes parameters are useless.\ninitial_population = population_vectors.copy()\n\nnum_parents_mating = 4 # Number of solutions to be selected as parents in the mating pool.\n\nnum_generations = 500 # Number of generations.\n\nmutation_percent_genes = 5 # Percentage of genes to mutate. This parameter has no action if the parameter mutation_num_genes exists.\n\nparent_selection_type = \"sss\" # Type of parent selection.\n\ncrossover_type = \"single_point\" # Type of the crossover operator.\n\nmutation_type = \"random\" # Type of the mutation operator.\n\nkeep_parents = 1 # Number of parents to keep in the next population. -1 means keep all parents and 0 means keep nothing.\n\ninit_range_low = -2\ninit_range_high = 5\n\nga_instance = pygad.GA(num_generations=num_generations, \n                       num_parents_mating=num_parents_mating, \n                       initial_population=initial_population,\n                       fitness_func=fitness_func,\n                       mutation_percent_genes=mutation_percent_genes,\n                       init_range_low=init_range_low,\n                       init_range_high=init_range_high,\n                       parent_selection_type=parent_selection_type,\n                       crossover_type=crossover_type,\n                       mutation_type=mutation_type,\n                       keep_parents=keep_parents,\n                       on_generation=callback_generation)\n\nga_instance.run()\n\n# After the generations complete, some plots are showed that summarize how the outputs/fitness values evolve over generations.\nga_instance.plot_fitness()\n\n# Returning the details of the best solution.\nsolution, solution_fitness, solution_idx = ga_instance.best_solution()\nprint(\"Parameters of the best solution : {solution}\".format(solution=solution))\nprint(\"Fitness value of the best solution = {solution_fitness}\".format(solution_fitness=solution_fitness))\nprint(\"Index of the best solution : {solution_idx}\".format(solution_idx=solution_idx))\n\nif ga_instance.best_solution_generation != -1:\n    print(\"Best fitness value reached after {best_solution_generation} generations.\".format(best_solution_generation=ga_instance.best_solution_generation))\n\n# Predicting the outputs of the data using the best solution.\npredictions = pygad.nn.predict(last_layer=GANN_instance.population_networks[solution_idx],\n                               data_inputs=data_inputs)\nprint(\"Predictions of the trained network : {predictions}\".format(predictions=predictions))\n\n# Calculating some statistics\nnum_wrong = numpy.where(predictions != data_outputs)[0]\nnum_correct = data_outputs.size - num_wrong.size\naccuracy = 100 * (num_correct/data_outputs.size)\nprint(\"Number of correct classifications : {num_correct}.\".format(num_correct=num_correct))\nprint(\"Number of wrong classifications : {num_wrong}.\".format(num_wrong=num_wrong.size))\nprint(\"Classification accuracy : {accuracy}.\".format(accuracy=accuracy))\n```\n\n# For More Information\n\nThere are different resources that can be used to get started with the genetic algorithm and building it in Python. \n\n## Tutorial: Implementing Genetic Algorithm in Python\n\nTo start with coding the genetic algorithm, you can check the tutorial titled [**Genetic Algorithm Implementation in Python**](https://www.linkedin.com/pulse/genetic-algorithm-implementation-python-ahmed-gad) available at these links:\n\n- [LinkedIn](https://www.linkedin.com/pulse/genetic-algorithm-implementation-python-ahmed-gad)\n- [Towards Data Science](https://towardsdatascience.com/genetic-algorithm-implementation-in-python-5ab67bb124a6)\n- [KDnuggets](https://www.kdnuggets.com/2018/07/genetic-algorithm-implementation-python.html)\n\n[This tutorial](https://www.linkedin.com/pulse/genetic-algorithm-implementation-python-ahmed-gad) is prepared based on a previous version of the project but it still a good resource to start with coding the genetic algorithm.\n\n[![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)\n\n## Tutorial: Introduction to Genetic Algorithm\n\nGet started with the genetic algorithm by reading the tutorial titled [**Introduction to Optimization with Genetic Algorithm**](https://www.linkedin.com/pulse/introduction-optimization-genetic-algorithm-ahmed-gad) which is available at these links:\n\n* [LinkedIn](https://www.linkedin.com/pulse/introduction-optimization-genetic-algorithm-ahmed-gad)\n* [Towards Data Science](https://www.kdnuggets.com/2018/03/introduction-optimization-with-genetic-algorithm.html)\n* [KDnuggets](https://towardsdatascience.com/introduction-to-optimization-with-genetic-algorithm-2f5001d9964b)\n\n[![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)\n\n## Tutorial: Build Neural Networks in Python\n\nRead about building neural networks in Python through the tutorial titled [**Artificial Neural Network Implementation using NumPy and Classification of the Fruits360 Image Dataset**](https://www.linkedin.com/pulse/artificial-neural-network-implementation-using-numpy-fruits360-gad) available at these links:\n\n* [LinkedIn](https://www.linkedin.com/pulse/artificial-neural-network-implementation-using-numpy-fruits360-gad)\n* [Towards Data Science](https://towardsdatascience.com/artificial-neural-network-implementation-using-numpy-and-classification-of-the-fruits360-image-3c56affa4491)\n* [KDnuggets](https://www.kdnuggets.com/2019/02/artificial-neural-network-implementation-using-numpy-and-image-classification.html)\n\n[![Building Neural Networks Python](https://user-images.githubusercontent.com/16560492/82078281-30472b80-96e1-11ea-8017-6a1f4383d602.jpg)](https://www.linkedin.com/pulse/artificial-neural-network-implementation-using-numpy-fruits360-gad)\n\n## Tutorial: Optimize Neural Networks with Genetic Algorithm\n\nRead about training neural networks using the genetic algorithm through the tutorial titled [**Artificial Neural Networks Optimization using Genetic Algorithm with Python**](https://www.linkedin.com/pulse/artificial-neural-networks-optimization-using-genetic-ahmed-gad) available at these links:\n\n- [LinkedIn](https://www.linkedin.com/pulse/artificial-neural-networks-optimization-using-genetic-ahmed-gad)\n- [Towards Data Science](https://towardsdatascience.com/artificial-neural-networks-optimization-using-genetic-algorithm-with-python-1fe8ed17733e)\n- [KDnuggets](https://www.kdnuggets.com/2019/03/artificial-neural-networks-optimization-genetic-algorithm-python.html)\n\n[![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)\n\n## Tutorial: Building CNN in Python\n\nTo start with coding the genetic algorithm, you can check the tutorial titled [**Building Convolutional Neural Network using NumPy from Scratch**](https://www.linkedin.com/pulse/building-convolutional-neural-network-using-numpy-from-ahmed-gad) available at these links:\n\n- [LinkedIn](https://www.linkedin.com/pulse/building-convolutional-neural-network-using-numpy-from-ahmed-gad)\n- [Towards Data Science](https://towardsdatascience.com/building-convolutional-neural-network-using-numpy-from-scratch-b30aac50e50a)\n- [KDnuggets](https://www.kdnuggets.com/2018/04/building-convolutional-neural-network-numpy-scratch.html)\n- [Chinese Translation](http://m.aliyun.com/yunqi/articles/585741)\n\n[This tutorial](https://www.linkedin.com/pulse/building-convolutional-neural-network-using-numpy-from-ahmed-gad)) is prepared based on a previous version of the project but it still a good resource to start with coding CNNs.\n\n[![Building CNN in Python](https://user-images.githubusercontent.com/16560492/82431022-6c3a1200-9a8e-11ea-8f1b-b055196d76e3.png)](https://www.linkedin.com/pulse/building-convolutional-neural-network-using-numpy-from-ahmed-gad)\n\n## Tutorial: Derivation of CNN from FCNN\n\nGet started with the genetic algorithm by reading the tutorial titled [**Derivation of Convolutional Neural Network from Fully Connected Network Step-By-Step**](https://www.linkedin.com/pulse/derivation-convolutional-neural-network-from-fully-connected-gad) which is available at these links:\n\n* [LinkedIn](https://www.linkedin.com/pulse/derivation-convolutional-neural-network-from-fully-connected-gad)\n* [Towards Data Science](https://towardsdatascience.com/derivation-of-convolutional-neural-network-from-fully-connected-network-step-by-step-b42ebafa5275)\n* [KDnuggets](https://www.kdnuggets.com/2018/04/derivation-convolutional-neural-network-fully-connected-step-by-step.html)\n\n[![Derivation of CNN from FCNN](https://user-images.githubusercontent.com/16560492/82431369-db176b00-9a8e-11ea-99bd-e845192873fc.png)](https://www.linkedin.com/pulse/derivation-convolutional-neural-network-from-fully-connected-gad)\n\n## Book: Practical Computer Vision Applications Using Deep Learning with CNNs\n\nYou can also check my book cited as [**Ahmed Fawzy Gad 'Practical Computer Vision Applications Using Deep Learning with CNNs'. Dec. 2018, Apress, 978-1-4842-4167-7**](https://www.amazon.com/Practical-Computer-Vision-Applications-Learning/dp/1484241665) which discusses neural networks, convolutional neural networks, deep learning, genetic algorithm, and more.\n\nFind the book at these links:\n\n- [Amazon](https://www.amazon.com/Practical-Computer-Vision-Applications-Learning/dp/1484241665)\n- [Springer](https://link.springer.com/book/10.1007/978-1-4842-4167-7)\n- [Apress](https://www.apress.com/gp/book/9781484241660)\n- [O'Reilly](https://www.oreilly.com/library/view/practical-computer-vision/9781484241677)\n- [Google Books](https://books.google.com.eg/books?id=xLd9DwAAQBAJ)\n\n![Fig04](https://user-images.githubusercontent.com/16560492/78830077-ae7c2800-79e7-11ea-980b-53b6bd879eeb.jpg)\n\n# Citing PyGAD - Bibtex Formatted Citation\n\nIf you used PyGAD, please consider adding a citation to the following paper about PyGAD:\n\n```\n@misc{gad2021pygad,\n      title={PyGAD: An Intuitive Genetic Algorithm Python Library}, \n      author={Ahmed Fawzy Gad},\n      year={2021},\n      eprint={2106.06158},\n      archivePrefix={arXiv},\n      primaryClass={cs.NE}\n}\n```\n\n# Contact Us\n\n* E-mail: ahmed.f.gad@gmail.com\n* [LinkedIn](https://www.linkedin.com/in/ahmedfgad)\n* [Amazon Author Page](https://amazon.com/author/ahmedgad)\n* [Heartbeat](https://heartbeat.fritz.ai/@ahmedfgad)\n* [Paperspace](https://blog.paperspace.com/author/ahmed)\n* [KDnuggets](https://kdnuggets.com/author/ahmed-gad)\n* [TowardsDataScience](https://towardsdatascience.com/@ahmedfgad)\n* [GitHub](https://github.com/ahmedfgad)\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fahmedfgad%2FNeuralGenetic","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fahmedfgad%2FNeuralGenetic","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fahmedfgad%2FNeuralGenetic/lists"}