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-`mutation`: Refers to the method that applies the mutation operator based on the selected type of mutation in the `mutation_type` property.
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-`select_parents`: Refers to a method that selects the parents based on the parent selection type specified in the `parent_selection_type` attribute.
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-`best_solutions_fitness`: A list holding the fitness values of the best solutions for all generation.
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-`best_solution_generation`: The generation number at which the best solution is reached. It is only assigned the generation number after the `run()` method completes. Otherwise, its value is -1.
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-`best_solution_generation`: The generation number at which the best fitness value is reached. It is only assigned the generation number after the `run()` method completes. Otherwise, its value is -1.
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-`cal_pop_fitness`: A method that calculates the fitness values for all solutions within the population by calling the function passed to the `fitness_func` parameter for each solution.
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Next, the steps of using the PyGAD library are discussed.
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To use PyGAD, here is a summary of the required steps:
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1. Preparing the `fitness_func` parameter.
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2. Preparing other parameters.
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3.Example of preparing the parameters.
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4.Import the `pygad.py` module.
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5.Create an instance of the `GA` class.
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6.Run the genetic algorithm.
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7.Plotting Results.
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2. Preparing Other Parameters.
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3.Import `pygad`.
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4.Create an Instance of the `pygad.GA` Class.
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5.Run the Genetic Algorithm.
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6.Plotting Results.
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7.Information about the Best Solution.
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8. Saving & Loading the Results.
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Let's discuss how to do each of these steps.
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By creating this function, you are ready to use the library.
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### Example of Preparing the Parameters
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### Preparing Other Parameters
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Here is an example for preparing the parameters:
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Here is an example for preparing the other parameters:
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```python
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num_generations =50
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mutation_percent_genes =10
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```
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#### Optional`callback_generation` Parameter
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#### The`callback_generation` Parameter
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In PyGAD 2.0.0 and higher, an optional parameter named `callback_generation` is supported which allow the user to call a function (with a single parameter) after each generation. Here is a simple function that just prints the current generation number and the fitness value of the best solution in the current generation. The `generations_completed` attribute of the GA class returns the number of the last completed generation.
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...)
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```
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After the parameters are prepared, we can import the `pygad`module and build an instance of the GA class.
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After the parameters are prepared, we can import the `pygad` and build an instance of the GA class.
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### Import the `pygad.py` Module
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### Import the `pygad`
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The next step is to import the `pygad` module as follows:
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The next step is to import `pygad` as follows:
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```python
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import pygad
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```
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This module has a class named `GA` which holds the implementation of all methods for running the genetic algorithm.
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PyGAD has a class named `GA` which holds the implementation of all methods for running the genetic algorithm.
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### Create an Instance of the `GA` Class.
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### Create an Instance of the `pygad.GA` Class.
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The `GA` class is instantiated where the previously prepared parameters are fed to its constructor. The constructor is responsible for creating the initial population.
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The `pygad.GA` class is instantiated where the previously prepared parameters are fed to its constructor. The constructor is responsible for creating the initial population.
print("Parameters of the best solution : {solution}".format(solution=solution))
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print("Fitness value of the best solution = {solution_fitness}".format(solution_fitness=solution_fitness))
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print("Index of the best solution : {solution_idx}".format(solution_idx=solution_idx))
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```
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Using the `best_solution_generation` attribute of the instance from the `pygad.GA` class, the generation number at which the **best fitness** is reached could be fetched.
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```python
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if ga_instance.best_solution_generation !=-1:
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print("Best fitness value reached after {best_solution_generation} generations.".format(best_solution_generation=ga_instance.best_solution_generation))
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```
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### Saving & Loading the Results
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After the `run()` method completes, it is possible to save the current instance of the genetic algorithm to avoid losing the progress made. The `save()` method is available for that purpose. According to the next code, a file named `genetic.pkl` will be created and saved in the current directory.
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After the `run()` method completes, it is possible to save the current instance of the genetic algorithm to avoid losing the progress made. The `save()` method is available for that purpose. Just pass the file name to it without an extension. According to the next code, a file named `genetic.pkl` will be created and saved in the current directory.
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```python
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# Saving the GA instance.
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filename ='genetic'# The filename to which the instance is saved. The name is without extension.
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filename ='genetic'
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ga_instance.save(filename=filename)
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```
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You can also load the saved model using the `load()` function and continue using it. For example, you might run the genetic algorithm for a number of generations, save its current state using the `save()` method, load the model using the `load()` function, and then call the `run()` method again.
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To 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:
[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.
You 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).
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Get 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:
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Read 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:
Read 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:
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You 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.
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