Release notes from GeneticAlgorithmPython

3.3.1: Merge pull request #278 from ahmedfgad/github-actions

2024-02-17T21:03:20Z

Merge pull request ahmedfgad#277 from ahmedfgad/master

3.3.0: Merge pull request #265 from ahmedfgad/github-actions

2024-01-29T06:29:41Z

GitHub actions

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

2023-09-08T00:33:25Z

GitHub actions

PyGAD 3.1.0 Release

2023-06-20T19:27:49Z

PyGAD 3.1.0 Release Notes

Fix a bug when the initial population has duplciate genes if a nested gene space is used.
The gene_space parameter can no longer be assigned a tuple.
Fix a bug when the gene_space parameter has a member of type tuple.
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).
Bug fixes when creating the initial population using gene_space attribute.
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.
Formatting the strings using f-string instead of the format() method. ahmedfgad#189
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
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.
Except for the pygad.nn module, the print() function in all other modules are replaced by the logging module to log messages.
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.
Fix a bug when adaptive mutation is used while fitness_batch_size>1. ahmedfgad#195
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.
Use probabilities to select parents using the rank parent selection method. ahmedfgad#205
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
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.
The data parameter in the predict() function of the pygad.kerasga module can be assigned a data generator. ahmedfgad#115 ahmedfgad#207
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
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

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 Notes

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.
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.
The pygad.utils.crossover module has a class named Crossover where all the crossover operators exist. The pygad.GA class extends this class.
The pygad.utils.mutation module has a class named Mutation where all the mutation operators exist. The pygad.GA class extends this class.
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.
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.

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

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 for more information.
A new instance attribute called logger to save the logger.
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. ahmedfgad#163
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. ahmedfgad#92 (comment)
Validate the value passed to the initial_population parameter.
Validate the type and length of the pop_fitness parameter of the best_solution() method.
Some edits in the documentation. ahmedfgad#106
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.
The summary() method returns the summary as a single-line string. Just log/print the returned string it to see it properly.
The callback_generation parameter is removed. Use the on_generation parameter instead.
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.
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 Notes

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 Notes

Add the cloudpickle library as a dependency.

2.18.3

2023-02-14T20:38:25Z

Update conf.py

2.18.2

2023-02-14T18:59:47Z

PyGAD 2.18.2