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3.2.0: Merge pull request #232 from ahmedfgad/github-actions

2023-09-08T00:33:25Z

Merge pull request ahmedfgad#232 from ahmedfgad/github-actions

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PyGAD 3.1.0 Release

2023-06-20T19:27:49Z

PyGAD 3.1.0 Release

PyGAD 3.1.0 Release Notes
1. Fix a bug when the initial population has duplciate genes if a nested gene space is used.
2. The gene_space parameter can no longer be assigned a tuple.
3. Fix a bug when the gene_space parameter has a member of type tuple.
4. A new instance attribute called gene_space_unpacked which has the unpacked gene_space. It is used to solve duplicates. For infinite ranges in the gene_space, they are unpacked to a limited number of values (e.g. 100).
5. Bug fixes when creating the initial population using gene_space attribute.
6. When a dict is used with the gene_space attribute, the new gene value was calculated by summing 2 values: 1) the value sampled from the dict 2) a random value returned from the random mutation range defined by the 2 parameters random_mutation_min_val and random_mutation_max_val. This might cause the gene value to exceed the range limit defined in the gene_space. To respect the gene_space range, this release only returns the value from the dict without summing it to a random value.
7. Formatting the strings using f-string instead of the format() method. ahmedfgad#189
8. In the __init__() of the pygad.GA class, the logged error messages are handled using a try-except block instead of repeating the logger.error() command. ahmedfgad#189
9. A new class named CustomLogger is created in the pygad.cnn module to create a default logger using the logging module assigned to the logger attribute. This class is extended in all other classes in the module. The constructors of these classes have a new parameter named logger which defaults to None. If no logger is passed, then the default logger in the CustomLogger class is used.
10. Except for the pygad.nn module, the print() function in all other modules are replaced by the logging module to log messages.
11. The callback functions/methods on_fitness(), on_parents(), on_crossover(), and on_mutation() can return values. These returned values override the corresponding properties. The output of on_fitness() overrides the population fitness. The on_parents() function/method must return 2 values representing the parents and their indices. The output of on_crossover() overrides the crossover offspring. The output of on_mutation() overrides the mutation offspring.
12. Fix a bug when adaptive mutation is used while fitness_batch_size>1. ahmedfgad#195
13. When allow_duplicate_genes=False and a user-defined gene_space is used, it sometimes happen that there is no room to solve the duplicates between the 2 genes by simply replacing the value of one gene by another gene. This release tries to solve such duplicates by looking for a third gene that will help in solving the duplicates. These examples explain how it works. Check this section for more information.
14. Use probabilities to select parents using the rank parent selection method. ahmedfgad#205
15. The 2 parameters random_mutation_min_val and random_mutation_max_val can accept iterables (list/tuple/numpy.ndarray) with length equal to the number of genes. This enables customizing the mutation range for each individual gene. ahmedfgad#198
16. The 2 parameters init_range_low and init_range_high can accept iterables (list/tuple/numpy.ndarray) with length equal to the number of genes. This enables customizing the initial range for each individual gene when creating the initial population.
17. The data parameter in the predict() function of the pygad.kerasga module can be assigned a data generator. ahmedfgad#115 ahmedfgad#207
18. The predict() function of the pygad.kerasga module accepts 3 optional parameters: 1) batch_size=None, verbose=0, and steps=None. Check documentation of the Keras Model.predict() method for more information. ahmedfgad#207
19. The documentation is updated to explain how mutation works when gene_space is used with int or float data types. Check this section. ahmedfgad#198

PyGAD 3.0.1

2023-04-20T13:35:05Z

PyGAD 3.0.1

Fix an issue with passing user-defined function/method for parent selection. ahmedfgad#179

PyGAD 3.0.0 Release

2023-04-08T19:16:22Z

PyGAD 3.0.0 Release

PyGAD 3.0.0 Release Notes
1. The structure of the library is changed and some methods defined in the `pygad.py` module are moved to the `pygad.utils`, `pygad.helper`, and `pygad.visualize` submodules.
2. The `pygad.utils.parent_selection` module has a class named `ParentSelection` where all the parent selection operators exist. The `pygad.GA` class extends this class.
3. The `pygad.utils.crossover` module has a class named `Crossover` where all the crossover operators exist. The `pygad.GA` class extends this class.
4. The `pygad.utils.mutation` module has a class named `Mutation` where all the mutation operators exist. The `pygad.GA` class extends this class.
5. The `pygad.helper.unique` module has a class named `Unique` some helper methods exist to solve duplicate genes and make sure every gene is unique. The `pygad.GA` class extends this class.
6. The `pygad.visualize.plot` module has a class named `Plot` where all the methods that create plots exist. The `pygad.GA` class extends this class.

```python
...
class GA(utils.parent_selection.ParentSelection,
utils.crossover.Crossover,
utils.mutation.Mutation,
helper.unique.Unique,
visualize.plot.Plot):
...
```

2. Support of using the `logging` module to log the outputs to both the console and text file instead of using the `print()` function. This is by assigning the `logging.Logger` to the new `logger` parameter. Check the [Logging Outputs](https://pygad.readthedocs.io/en/latest/README_pygad_ReadTheDocs.html#logging-outputs) for more information.
3. A new instance attribute called `logger` to save the logger.
4. The function/method passed to the `fitness_func` parameter accepts a new parameter that refers to the instance of the `pygad.GA` class. Check this for an example: [Use Functions and Methods to Build Fitness Function and Callbacks](https://pygad.readthedocs.io/en/latest/README_pygad_ReadTheDocs.html#use-functions-and-methods-to-build-fitness-and-callbacks). ahmedfgad#163
5. Update the documentation to include an example of using functions and methods to calculate the fitness and build callbacks. Check this for more details: [Use Functions and Methods to Build Fitness Function and Callbacks](https://pygad.readthedocs.io/en/latest/README_pygad_ReadTheDocs.html#use-functions-and-methods-to-build-fitness-and-callbacks). ahmedfgad#92 (comment)
6. Validate the value passed to the `initial_population` parameter.
7. Validate the type and length of the `pop_fitness` parameter of the `best_solution()` method.
8. Some edits in the documentation. ahmedfgad#106
9. Fix an issue when building the initial population as (some) genes have their value taken from the mutation range (defined by the parameters `random_mutation_min_val` and `random_mutation_max_val`) instead of using the parameters `init_range_low` and `init_range_high`.
10. The `summary()` method returns the summary as a single-line string. Just log/print the returned string it to see it properly.
11. The `callback_generation` parameter is removed. Use the `on_generation` parameter instead.
12. There was an issue when using the `parallel_processing` parameter with Keras and PyTorch. As Keras/PyTorch are not thread-safe, the `predict()` method gives incorrect and weird results when more than 1 thread is used. ahmedfgad#145 ahmedfgad/TorchGA#5 ahmedfgad/KerasGA#6. Thanks to this [StackOverflow answer](https://stackoverflow.com/a/75606666/5426539).
13. Replace `numpy.float` by `float` in the 2 parent selection operators roulette wheel and stochastic universal. ahmedfgad#168

PyGAD 2.19.2 Release

2023-02-23T13:36:28Z

PyGAD 2.19.2 Release

PyGAD 2.19.2 Release Notes
1. Fix an issue when paralell processing was used where the elitism solutions' fitness values are not re-used. ahmedfgad#160 (comment)

PyGAD 2.19.1 Release

2023-02-22T15:20:14Z

PyGAD 2.19.1 Release

PyGAD 2.19.1 Release Notes
1. Add the [cloudpickle](https://github.com/cloudpipe/cloudpickle) library as a dependency.

2.18.3

2023-02-14T20:38:25Z

2.18.2

2023-02-14T18:59:47Z

2.18.1

2022-09-19T19:06:36Z

PyGAD 2.18.0 Documentation

2022-09-09T05:38:58Z

PyGAD 2.18.0 Documentation

1. Raise an exception if the sum of fitness values is zero while either roulette wheel or stochastic universal parent selection is used. ahmedfgad#129
2. Initialize the value of the `run_completed` property to `False`. ahmedfgad#122
3. The values of these properties are no longer reset with each call to the `run()` method `self.best_solutions, self.best_solutions_fitness, self.solutions, self.solutions_fitness`: ahmedfgad#123. Now, the user can have the flexibility of calling the `run()` method more than once while extending the data collected after each generation. Another advantage happens when the instance is loaded and the `run()` method is called, as the old fitness value are shown on the graph alongside with the new fitness values. Read more in this section: [Continue without Loosing Progress](https://pygad.readthedocs.io/en/latest/README_pygad_ReadTheDocs.html#continue-without-loosing-progress)
4. Thanks [Prof. Fernando Jiménez Barrionuevo](http://webs.um.es/fernan) (Dept. of Information and Communications Engineering, University of Murcia, Murcia, Spain) for editing this [comment](https://github.com/ahmedfgad/GeneticAlgorithmPython/blob/5315bbec02777df96ce1ec665c94dece81c440f4/pygad.py#L73) in the code. ahmedfgad@5315bbe
5. A bug fixed when `crossover_type=None`.
6. Support of elitism selection through a new parameter named `keep_elitism`. It defaults to 1 which means for each generation keep only the best solution in the next generation. If assigned 0, then it has no effect. Read more in this section: [Elitism Selection](https://pygad.readthedocs.io/en/latest/README_pygad_ReadTheDocs.html#elitism-selection). ahmedfgad#74
7. A new instance attribute named `last_generation_elitism` added to hold the elitism in the last generation.
8. A new parameter called `random_seed` added to accept a seed for the random function generators. Credit to this issue ahmedfgad#70 and [Prof. Fernando Jiménez Barrionuevo](http://webs.um.es/fernan). Read more in this section: [Random Seed](https://pygad.readthedocs.io/en/latest/README_pygad_ReadTheDocs.html#random-seed).
9. Editing the `pygad.TorchGA` module to make sure the tensor data is moved from GPU to CPU. Thanks to Rasmus Johansson for opening this pull request: ahmedfgad/TorchGA#2