8000 PyGAD 2.15.0 · lkampoli/GeneticAlgorithmPython@9b4eee3 · GitHub
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PyGAD 2.15.0
1. Control the precision of all genes/individual genes. Thanks to Rainer for asking about this feature: ahmedfgad#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. ahmedfgad#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: ahmedfgad#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". ahmedfgad#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.
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‎README.md

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@@ -181,18 +181,6 @@ num_parents_mating = 7 # Number of solutions to be selected as parents in the ma
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sol_per_pop = 50 # Number of solutions in the population.
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num_genes = len(function_inputs)
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init_range_low = -2
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init_range_high = 5
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parent_selection_type = "sss" # Type of parent selection.
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keep_parents = 7 # Number of parents to keep in the next population. -1 means keep all parents and 0 means keep nothing.
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crossover_type = "single_point" # Type of the crossover operator.
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# Parameters of the mutation operation.
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mutation_type = "random" # Type of the mutation operator.
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mutation_percent_genes = 10 # Percentage of genes to mutate. This parameter has no action if the parameter mutation_num_genes exists or when mutation_type is None.
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last_fitness = 0
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def callback_generation(ga_instance):
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global last_fitness
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fitness_func=fitness_function,
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sol_per_pop=sol_per_pop,
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num_genes=num_genes,
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init_range_low=init_range_low,
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init_range_high=init_range_high,
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parent_selection_type=parent_selection_type,
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keep_parents=keep_parents,
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crossover_type=crossover_type,
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mutation_type=mutation_type,
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mutation_percent_genes=mutation_percent_genes,
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on_generation=callback_generation)
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# Running the GA to optimize the parameters of the function.
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ga_instance.run()
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# After the generations complete, some plots are showed that summarize the how the outputs/fitenss values evolve over generations.
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ga_instance.plot_result()
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ga_instance.plot_fitness()
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# Returning the details of the best solution.
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solution, solution_fitness, solution_idx = ga_instance.best_solution()
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# Loading the saved GA instance.
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loaded_ga_instance = pygad.load(filename=filename)
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loaded_ga_instance.plot_result()
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loaded_ga_instance.plot_fitness()
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```
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# For More Information

‎__init__.py

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from .pygad import * # Relative import.
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__version__ = "2.14.3"
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__version__ = "2.15.0"

‎example.py

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# Running the GA to optimize the parameters of the function.
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ga_instance.run()
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ga_instance.plot_result()
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ga_instance.plot_fitness()
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# Returning the details of the best solution.
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solution, solution_fitness, solution_idx = ga_instance.best_solution(ga_instance.last_generation_fitness)
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# Loading the saved GA instance.
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loaded_ga_instance = pygad.load(filename=filename)
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loaded_ga_instance.plot_result()
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loaded_ga_instance.plot_fitness()

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