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PyGAD-2.10.0
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@ahmedfgad ahmedfgad released this 04 Jan 02:17
· 357 commits to master since this release
2.10.0
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Learn about vigilant mode.
  1. Support of a new module pygad.torchga to train PyTorch models using PyGAD. Check its documentation.
  2. Support of adaptive mutation where the mutation rate is determined by the fitness value of each solution. Read the Adaptive Mutation section for more details. Also, read this paper: Libelli, S. Marsili, and P. Alba. "Adaptive mutation in genetic algorithms." Soft computing 4.2 (2000): 76-80.
  3. Before the run() method completes or exits, the fitness value of the best solution in the current population is appended to the best_solution_fitness list attribute. Note that the fitness value of the best solution in the initial population is already saved at the beginning of the list. So, the fitness value of the best solution is saved before the genetic algorithm starts and after it ends.
  4. When the parameter parent_selection_type is set to sss (steady-state selection), then a warning message is printed if the value of the keep_parents parameter is set to 0.
  5. More validations to the user input parameters.
  6. The default value of the mutation_percent_genes is set to the string "default" rather than the integer 10. This change helps to know whether the user explicitly passed a value to the mutation_percent_genes parameter or it is left to its default one. The "default" value is later translated into the integer 10.
  7. The mutation_percent_genes parameter is no longer accepting the value 0. It must be >0 and <=100.
  8. The built-in warnings module is used to show warning messages rather than just using the print() function.
  9. A new bool parameter called suppress_warnings is added to the constructor of the pygad.GA class. It allows the user to control whether the warning messages are printed or not. It defaults to False which means the messages are printed.
  10. A helper method called adaptive_mutation_population_fitness() is created to calculate the average fitness value used in adaptive mutation to filter the solutions.
  11. The best_solution() method accepts a new optional parameter called pop_fitness. It accepts a list of the fitness values of the solutions in the population. If None, then the cal_pop_fitness() method is called to calculate the fitness values of the population.
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