Release notes from GeneticAlgorithmPython

2.18.1

2022-09-19T19:06:36Z

PyGAD 2.18.1 Documentation

PyGAD 2.18.0 Documentation

2022-09-09T05:38:58Z

Raise an exception if the sum of fitness values is zero while either roulette wheel or stochastic universal parent selection is used. ahmedfgad#129
Initialize the value of the run_completed property to False. ahmedfgad#122
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
Thanks Prof. Fernando Jiménez Barrionuevo (Dept. of Information and Communications Engineering, University of Murcia, Murcia, Spain) for editing this comment in the code. ahmedfgad@5315bbe
A bug fixed when crossover_type=None.
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. ahmedfgad#74
A new instance attribute named last_generation_elitism added to hold the elitism in the last generation.
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. Read more in this section: Random Seed.
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

2.17.0

2022-07-08T03:55:54Z

Change version to 2.17.0

2.16.3

2022-02-03T01:34:29Z

PyGAD 2.16.3 Documentation

2.16.1

2021-09-29T01:45:01Z

PyGAD 2.16.1 Documentation

PyGAD 2.16.0 Documentation

2021-06-20T02:17:27Z

A user-defined function can be passed to the mutation_type, crossover_type, and parent_selection_type parameters in the pygad.GA class to create a custom mutation, crossover, and parent selection operators. Check the User-Defined Crossover, Mutation, and Parent Selection Operators section in the documentation: https://pygad.readthedocs.io/en/latest/README_pygad_ReadTheDocs.html#user-defined-crossover-mutation-and-parent-selection-operators

The example_custom_operators.py script gives an example of building and using custom functions for the 3 operators.

ahmedfgad#50

PyGAD 2.15.1 Documentation

2021-06-18T06:16:34Z

Fix a bug when keep_parents is set to a positive integer. ahmedfgad#49

PyGAD 2.15.0 Documentation

2021-06-18T03:08:12Z

Changes in PyGAD 2.15.0

Control the precision of all genes/individual genes. Thanks to Rainer for asking about this feature: ahmedfgad#43 (comment)
A new attribute named last_generation_parents_indices holds the indices of the selected parents in the last generation.
In adaptive mutation, no need to recalculate the fitness values of the parents selected in the last generation as these values can be returned based on the last_generation_fitness and last_generation_parents_indices attributes. This speeds-up the adaptive mutation.
When a sublist has a value of None in the gene_space parameter (e.g. gene_space=[[1, 2, 3], [5, 6, None]]), then its value will be randomly generated for each solution rather than being generated once for all solutions. Previously, a value of None in a sublist of the gene_space parameter was identical across all solutions.
The dictionary assigned to the gene_space parameter itself or one of its elements has a new key called "step" to specify the step of moving from the start to the end of the range specified by the 2 existing keys "low" and "high". An example is {"low": 0, "high": 30, "step": 2} to have only even values for the gene(s) starting from 0 to 30. For more information, check the More about the gene_space Parameter section. ahmedfgad#48
A new function called predict() is added in both the pygad.kerasga and pygad.torchga modules to make predictions. This makes it easier than using custom code each time a prediction is to be made.
A new parameter called stop_criteria allows the user to specify one or more stop criteria to stop the evolution based on some conditions. Each criterion is passed as str which has a stop word. The current 2 supported words are reach and saturate. reach stops the run() method if the fitness value is equal to or greater than a given fitness value. An example for reach is "reach_40" which stops the evolution if the fitness is >= 40. saturate means stop the evolution if the fitness saturates for a given number of consecutive generations. An example for saturate is "saturate_7" which means stop the run() method if the fitness does not change for 7 consecutive generations. Thanks to Rainer for asking about this feature: ahmedfgad#44
A new bool parameter, defaults to False, named save_solutions is added to the constructor of the pygad.GA class. If True, then all solutions in each generation are appended into an attribute called solutions which is NumPy array.
The plot_result() method is renamed to plot_fitness(). The users should migrate to the new name as the old name will be removed in the future.
Four new optional parameters are added to the plot_fitness() function in the pygad.GA class which are font_size=14, save_dir=None, color="#3870FF", and plot_type="plot". Use font_size to change the font of the plot title and labels. save_dir accepts the directory to which the figure is saved. It defaults to None which means do not save the figure. color changes the color of the plot. plot_type changes the plot type which can be either "plot" (default), "scatter", or "bar". ahmedfgad#47
The default value of the title parameter in the plot_fitness() method is "PyGAD - Generation vs. Fitness" rather than "PyGAD - Iteration vs. Fitness".
A new method named plot_new_solution_rate() creates, shows, and returns a figure showing the rate of new/unique solutions explored in each generation. It accepts the same parameters as in the plot_fitness() method. This method only works when save_solutions=True in the pygad.GA class's constructor.
A new method named plot_genes() creates, shows, and returns a figure to show how each gene changes per each generation. It accepts similar parameters like the plot_fitness() method in addition to the graph_type, fill_color, and solutions parameters. The graph_type parameter can be either "plot" (default), "boxplot", or "histogram". fill_color accepts the fill color which works when graph_type is either "boxplot" or "histogram". solutions can be either "all" or "best" to decide whether all solutions or only best solutions are used.
The gene_type parameter now supports controlling the precision of float data types. For a gene, rather than assigning just the data type like float, assign a list/tuple/numpy.ndarray with 2 elements where the first one is the type and the second one is the precision. For example, [float, 2] forces a gene with a value like 0.1234 to be 0.12. For more information, check the More about the gene_type Parameter section.

Documentation of PyGAD 2.14.3

2021-06-06T06:21:19Z

The new release has some bug fixes when setting the save_best_solutions parameter to True. Previously, the best solution for generation i was added into the best_solutions attribute at generation i+1. Now, the best_solutions attribute is updated by each solution at its exact generation.

2.14.2: New bade added

2021-05-27T23:51:39Z

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