PyGAD-2.18.3

Update conf.py

PyGAD-2.18.2

PyGAD 2.18.2 release notes

1. Remove numpy.int and numpy.float from the list of supported data types. #151 #152
2. Call the on_crossover() callback function even if crossover_type is None. #138
3. Call the on_mutation() callback function even if mutation_type is None. #138

PyGAD-2.18.1

PyGAD 2.18.1

1. A big fix when keep_elitism is used. #132

PyGAD-2.18.0

1. Raise an exception if the sum of fitness values is zero while either roulette wheel or stochastic universal parent selection is used. #129
2. Initialize the value of the run_completed property to False. #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: #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. 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). #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 #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

PyGAD-2.17.0

PyGAD 2.17.0

Release Date: 8 July 2022

1. An issue is solved when the gene_space parameter is given a fixed value. e.g. gene_space=[range(5), 4]. The second gene's value is static (4) which causes an exception.
2. Fixed the issue where the allow_duplicate_genes parameter did not work when mutation is disabled (i.e. mutation_type=None). This is by checking for duplicates after crossover directly. #39
3. Solve an issue in the tournament_selection() method as the indices of the selected parents were incorrect. #89
4. Reuse the fitness values of the previously explored solutions rather than recalculating them. This feature only works if save_solutions=True.
5. Parallel processing is supported. This is by the introduction of a new parameter named parallel_processing in the constructor of the pygad.GA class. Thanks to [@windowshopr](https://github.com/windowshopr) for opening the issue [#78](#78) at GitHub. Check the [Parallel Processing in PyGAD](https://pygad.readthedocs.io/en/latest/README_pygad_ReadTheDocs.html#parallel-processing-in-pygad) section for more information and examples.

PyGAD-2.16.3

Changes in PyGAD 2.16.3

1. A new instance attribute called previous_generation_fitness added in the pygad.GA class. It holds the fitness values of one generation before the fitness values saved in the last_generation_fitness.
2. Issue in the cal_pop_fitness() method in getting the correct indices of the previous parents. This is solved by using the previous generation's fitness saved in the new attribute previous_generation_fitness to return the parents' fitness values. Thanks to Tobias Tischhauser (M.Sc. - [Mitarbeiter Institut EMS, Departement Technik, OST – Ostschweizer Fachhochschule, Switzerland](https://www.ost.ch/de/forschung-und-dienstleistungen/technik/systemtechnik/ems/team)) for detecting this bug.
3. Validate the fitness value returned from the fitness function. An exception is raised if something is wrong. #67

PyGAD-2.16.1

1. Reuse the fitness of previously explored solutions rather than recalculating them. This feature only works if save_solutions=True.
2. The user can use the tqdm library to show a progress bar. #50

import pygad
import numpy
import tqdm

equation_inputs = [4,-2,3.5]
desired_output = 44

def fitness_func(solution, solution_idx):
    output = numpy.sum(solution * equation_inputs)
    fitness = 1.0 / (numpy.abs(output - desired_output) + 0.000001)
    return fitness

num_generations = 10000
with tqdm.tqdm(total=num_generations) as pbar:
    ga_instance = pygad.GA(num_generations=num_generations,
                           sol_per_pop=5,
                           num_parents_mating=2,
                           num_genes=len(equation_inputs),
                           fitness_func=fitness_func,
                           on_generation=lambda _: pbar.update(1))
    
    ga_instance.run()

ga_instance.plot_result()

3. Solved the issue of unequal length between the solutions and solutions_fitness when the save_solutions parameter is set to True. Now, the fitness of the last population is appended to the solutions_fitness array. #64
4. There was an issue of getting the length of these 4 variables (solutions, solutions_fitness, best_solutions, and best_solutions_fitness) doubled after each call of the run() method. This is solved by resetting these variables at the beginning of the run() method. #62
5. Bug fixes when adaptive mutation is used (mutation_type="adaptive"). #65

PyGAD-2.16.0

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.
#50

PyGAD-2.15.1

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

PyGAD-2.15.0

1. Control the precision of all genes/individual genes. Thanks to Rainer for asking about this feature: #43 (comment)
2. A new attribute named last_generation_parents_indices holds the indices of the selected parents in the last generation.
3. 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.
4. 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.
5. 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. #48
6. 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.
7. 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: #44
8. 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.
9. 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.
10. 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". #47
11. The default value of the title parameter in the plot_fitness() method is "PyGAD - Generation vs. Fitness" rather than "PyGAD - Iteration vs. Fitness".
12. 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.
13. 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.
14. 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.