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PyGAD-3.2.0
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@ahmedfgad ahmedfgad released this 08 Sep 00:47
· 126 commits to master since this release
3.2.0
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Learn about vigilant mode.
  1. A new module pygad.utils.nsga2 is created that has the NSGA2 class that includes the functionalities of NSGA-II. The class has these methods: 1) get_non_dominated_set() 2) non_dominated_sorting() 3) crowding_distance() 4) sort_solutions_nsga2(). Check [this section](https://pygad.readthedocs.io/en/latest/pygad_more.html#multi-objective-optimization) for an example.
  2. Support of multi-objective optimization using Non-Dominated Sorting Genetic Algorithm II (NSGA-II) using the NSGA2 class in the pygad.utils.nsga2 module. Just return a list, tuple, or numpy.ndarray from the fitness function and the library will consider the problem as multi-objective optimization. All the objectives are expected to be maximization. Check [this section](https://pygad.readthedocs.io/en/latest/pygad_more.html#multi-objective-optimization) for an example.
  3. The parent selection methods and adaptive mutation are edited to support multi-objective optimization.
  4. Two new NSGA-II parent selection methods are supported in the pygad.utils.parent_selection module: 1) Tournament selection for NSGA-II 2) NSGA-II selection.
  5. The plot_fitness() method in the pygad.plot module has a new optional parameter named label to accept the label of the plots. This is only used for multi-objective problems. Otherwise, it is ignored. It defaults to None and accepts a list, tuple, or numpy.ndarray. The labels are used in a legend inside the plot.
  6. The default color in the methods of the pygad.plot module is changed to the greenish #64f20c color.
  7. A new instance attribute named pareto_fronts added to the pygad.GA instances that holds the pareto fronts when solving a multi-objective problem.
  8. The gene_type accepts a list, tuple, or numpy.ndarray for integer data types given that the precision is set to None (e.g. gene_type=[float, [int, None]]).
  9. In the cal_pop_fitness() method, the fitness value is re-used if save_best_solutions=True and the solution is found in the best_solutions attribute. These parameters also can help re-using the fitness of a solution instead of calling the fitness function: keep_elitism, keep_parents, and save_solutions.
  10. The value 99999999999 is replaced by float('inf') in the 2 methods wheel_cumulative_probs() and stochastic_universal_selection() inside the pygad.utils.parent_selection.ParentSelection class.
  11. The plot_result() method in the pygad.visualize.plot.Plot class is removed. Instead, please use the plot_fitness() if you did not upgrade yet.
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