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"reach" stop criteria with negative valued fitness function #296

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Tropingenie opened this issue Jul 12, 2024 · 1 comment
Open

"reach" stop criteria with negative valued fitness function #296

Tropingenie opened this issue Jul 12, 2024 · 1 comment
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@Tropingenie
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Tropingenie commented Jul 12, 2024

I am getting a ValueError: The value following the stop word in the 'stop_criteria' parameter must be a number but the value (-0.5) of type <class 'str'> found when trying to input a stop criteria with negative valued fitness function stop_criteria=["saturate_10", "reach_-0.5"],. I am unable to find any documentation on how the criteria "groups" numbers, and would rather not have to trawl through the code myself to find out.

If this feature is supported it would be appreciated if someone could explain how, otherwise, potentially a future feature. Until then I can always use the multiplicative inverse instead, but it reduces the readability of my outputs ("0.5" is an exact dollar error in my code, "2" is the inverse and less useful on its own).

@ahmedfgad ahmedfgad added the bug Something isn't working label Jan 7, 2025
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Fixed and will be supported in the next release!

ahmedfgad added a commit that referenced this issue Jan 7, 2025
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
@ahmedfgad ahmedfgad mentioned this issue Jan 7, 2025
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