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Solution_FItness array and solutions arrays are in different length. #64

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javid-b opened this issue Sep 13, 2021 · 4 comments
Open

Solution_FItness array and solutions arrays are in different length. #64

javid-b opened this issue Sep 13, 2021 · 4 comments
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@ahmedfgad
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@javid-b
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javid-b commented Sep 13, 2021

I am using pygad, for GA, to find combination of solutions which would satisfy conditions. I have got a code, which runs 15 generations with 40 populations. When GA stops running, the size of array is 640 where as array is 600. I am looking for a single array which would have solutions for all trials with fitness array next to it. However, i was expecting them to be equal. May be i am doing something wrong?
@urowietu
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urowietu commented Sep 13, 2021 via email

@javid-b
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javid-b commented Sep 16, 2021

Hi Keith,
Thanks for response. I did not quite understood, why 640/40 would give 15?
Yes, you are right i have got 15 genes and 40 competing algos in a generation.
Regardless if it gives 600 or 640, my expectation would be that number of iterated solutions would be equal to number of fitness values. Sorry, if i am saying something non-sensible.

Regards,
Javid

@urowietu
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urowietu commented Sep 19, 2021 via email

@ahmedfgad ahmedfgad self-assigned this Sep 28, 2021
@ahmedfgad ahmedfgad added the bug Something isn't working label Sep 28, 2021
@ahmedfgad
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Hi @javid-b,

Thanks for opening this issue. You are right as the fitness of the last population was not saved in the solutions_fitness list. This is solved by adding the next code at the end of the run() method.

if self.save_solutions:
    self.solutions_fitness.extend(self.last_generation_fitness)

The project will be updated soon and a new release of PyGAD will be published too.

Please let me know if you have any bugs or enhancements.

ahmedfgad added a commit that referenced this issue Sep 29, 2021
@ahmedfgad
PyGAD 2.16.1
19bac06 
1. Reuse the fitness of previously explored solutions rather than recalculating them. This feature only works if `save_solutions=True`.
2. The user can use the `tqdm` library to show a progress bar. #50

```python
import pygad
import numpy
import tqdm

equation_inputs = [4,-2,3.5]
desired_output = 44

def fitness_func(solution, solution_idx):
    output = numpy.sum(solution * equation_inputs)
    fitness = 1.0 / (numpy.abs(output - desired_output) + 0.000001)
    return fitness

num_generations = 10000
with tqdm.tqdm(total=num_generations) as pbar:
    ga_instance = pygad.GA(num_generations=num_generations,
                           sol_per_pop=5,
                           num_parents_mating=2,
                           num_genes=len(equation_inputs),
                           fitness_func=fitness_func,
                           on_generation=lambda _: pbar.update(1))
    
    ga_instance.run()

ga_instance.plot_result()
```

3. Solved the issue of unequal length between the `solutions` and `solutions_fitness` when the `save_solutions` parameter is set to `True`. Now, the fitness of the last population is appended to the `solutions_fitness` array. #64
4. There was an issue of getting the length of these 4 variables (`solutions`, `solutions_fitness`, `best_solutions`, and `best_solutions_fitness`) doubled after each call of the `run()` method. This is solved by resetting these variables at the beginning of the `run()` method. #62
5. Bug fixes when adaptive mutation is used (`mutation_type="adaptive"`). #65
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