Exception in GA, when setting adaptive mutation and batch fitness simultaneously #195
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ahmedfgad
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Fix a bug when `fitness_batch_size` is used with adaptive mutation: #195
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Thanks for opening the issue. There was a bug and it is fixed. It will be available in the new release of the library. |
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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. #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. #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. #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. #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. #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. #115 #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. #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. #198
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Hi,
I'm trying to have adaptive mutation while computing the fitness in batches. Apparently, it works fine, until it computes all the batches in current population, but then it tries to input an 1D array with the length of the total number of genes.
Thanks!
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