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initial_population
not effectively used/retained for multiobjective problems?
#279
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Makes sense. Can you please provide referene(s) for |
These are some resources that describes tournament selection for NSGA-II:
You can definitely have more resources. |
1. The `delay_after_gen` parameter is removed from the `pygad.GA` class constructor. As a result, it is no longer an attribute of the `pygad.GA` class instances. To add a delay after each generation, apply it inside the `on_generation` callback. #283 2. In the `single_point_crossover()` method of the `pygad.utils.crossover.Crossover` class, all the random crossover points are returned before the `for` loop. This is by calling the `numpy.random.randint()` function only once before the loop to generate all the K points (where K is the offspring size). This is compared to calling the `numpy.random.randint()` function inside the `for` loop K times, once for each individual offspring. 3. Bug fix in the `examples/example_custom_operators.py` script. #285 4. While making prediction using the `pygad.torchga.predict()` function, no gradients are calculated. 5. The `gene_type` parameter of the `pygad.helper.unique.Unique.unique_int_gene_from_range()` method accepts the type of the current gene only instead of the full gene_type list. 6. Created a new method called `unique_float_gene_from_range()` inside the `pygad.helper.unique.Unique` class to find a unique floating-point number from a range. 7. Fix a bug in the `pygad.helper.unique.Unique.unique_gene_by_space()` method to return the numeric value only instead of a NumPy array. 8. Refactoring the `pygad/helper/unique.py` script to remove duplicate codes and reformatting the docstrings. 9. The plot_pareto_front_curve() method added to the pygad.visualize.plot.Plot class to visualize the Pareto front for multi-objective problems. It only supports 2 objectives. #279 10. Fix a bug converting a nested NumPy array to a nested list. #300 11. The `Matplotlib` library is only imported when a method inside the `pygad/visualize/plot.py` script is used. This is more efficient than using `import matplotlib.pyplot` at the module level as this causes it to be imported when `pygad` is imported even when it is not needed. #292 12. Fix a bug when minus sign (-) is used inside the `stop_criteria` parameter (e.g. `stop_criteria=["saturate_10", "reach_-0.5"]`). #296 13. Make sure `self.best_solutions` is a list of lists inside the `cal_pop_fitness` method. #293 14. Fix a bug where the `cal_pop_fitness()` method was using the `previous_generation_fitness` attribute to return the parents fitness. This instance attribute was not using the fitness of the latest population, instead the fitness of the population before the last one. The issue is solved by updating the `previous_generation_fitness` attribute to the latest population fitness before the GA completes. #291
Hi,
Thanks for PyGAD, it's a great resource.
I am trying to use it for an multiobjective optimization task. I have a good idea for what good solutions could be so I am providing them via the
initial_population
argument. However, some of those solutions are bot being used, I think.My set up is as follows.
The image shows the final front 0 as blue dots and the red cross as one of the initial solutions that I provided whiuch apparently "disappeared".
I will appreciate any tips on how to set this up properly using PyGAD, thanks!
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