allow_duplicate_genes not working #39
Comments
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Hi, Thanks for using PyGAD! I have some comments on your code: I am using the latest version of PyGAD and I did not see any duplicates while This is the code I tested where I find the difference between the following 2 sets:
As long as you use 32 genes and the gene space has only 32 values, then it is expected that the difference between those 2 sets must be empty. This is what happens in my code. So, I think there is no issue with the If my code does not reflect yours, please let me know. import pygad
import numpy as np
def fitness(sol, idx):
ss = set(np.unique(sol))
r = set(np.arange(0,32,1)) - ss
print(r)
if len(r) > 0 :
print("\n\nSomething is WRONG\n\n")
return np.random.rand()
ga_instance = pygad.GA(num_generations=50,
num_parents_mating=2,
fitness_func=fitness,
init_range_low=0,
init_range_high=32,
sol_per_pop = 10,
num_genes=32,
gene_space=np.arange(0,32,1),
gene_type=int,
allow_duplicate_genes=False)
ga_instance.run()
solution, solution_fitness, solution_idx = ga_instance.best_solution()
# print(f'{solution}')
solution.sort(axis=0)
# print(solution)
ss = set(np.unique(solution))
r = set(np.arange(0,32,1)) - ss
print(r)
if len(r) > 0 :
print("\n\nSomething is WRONG\n\n") |
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Yes, it is working. The cause was lack of a gene_space parameter . Thank you for the response and this amazing library |
And i getting solutions with duplicated genes like: [10 3 14 4 17 10 5 0 7 6 11 8 15 16 13 1 14 4 2 9]] Mutation is not enabled, but i guess there is something i'm missing... Should allow_duplicate_genes also block duplicates after the mating? Thank |
@KevinGalassi, But for crossover, it only combines the genes from 2 solutions. Crossover is not meant to introduce new gene values by its own. But I think it would be a good feature to support. A warning maybe used if mutation is disabled while |
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My bad, when I looked at the wiki I haven't found this information explicitly declared. I avoided mutation because the possibility of multiple genes with the same value, but the same problem may arise with crossover too. BTW I'm trying to solve a kind of 'Travelling Saleman Problem', guess I'll look online. Thanks |
## PyGAD 2.17.0 Release Date: 8 July 2022 1. An issue is solved when the `gene_space` parameter is given a fixed value. e.g. gene_space=[range(5), 4]. The second gene's value is static (4) which causes an exception. 2. Fixed the issue where the `allow_duplicate_genes` parameter did not work when mutation is disabled (i.e. `mutation_type=None`). This is by checking for duplicates after crossover directly. #39 3. Solve an issue in the `tournament_selection()` method as the indices of the selected parents were incorrect. #89 4. Reuse the fitness values of the previously explored solutions rather than recalculating them. This feature only works if `save_solutions=True`. 5. Parallel processing is supported. This is by the introduction of a new parameter named `parallel_processing` in the constructor of the `pygad.GA` class. Thanks to [@windowshopr](https://github.com/windowshopr) for opening the issue [#78](#78) at GitHub. Check the [Parallel Processing in PyGAD](https://pygad.readthedocs.io/en/latest/README_pygad_ReadTheDocs.html#parallel-processing-in-pygad) section for more information and examples.
PyGAD 2.17.0 Release Date: 8 July 2022 1. An issue is solved when the `gene_space` parameter is given a fixed value. e.g. gene_space=[range(5), 4]. The second gene's value is static (4) which causes an exception. 2. Fixed the issue where the `allow_duplicate_genes` parameter did not work when mutation is disabled (i.e. `mutation_type=None`). This is by checking for duplicates after crossover directly. #39 3. Solve an issue in the `tournament_selection()` method as the indices of the selected parents were incorrect. #89 4. Reuse the fitness values of the previously explored solutions rather than recalculating them. This feature only works if `save_solutions=True`. 5. Parallel processing is supported. This is by the introduction of a new parameter named `parallel_processing` in the constructor of the `pygad.GA` class. Thanks to [@windowshopr](https://github.com/windowshopr) for opening the issue [#78](#78) at GitHub. Check the [Parallel Processing in PyGAD](https://pygad.readthedocs.io/en/latest/README_pygad_ReadTheDocs.html#parallel-processing-in-pygad) section for more information and examples.
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I might be doing something wrong but allow_duplicate_genes=False is not working for me, even the best solutions for the fitness function I am using have duplicate genes. For my case I am trying a fitness function that takes around 20 min, but here with a dummy fitness function also returns solutions with duplicated genes as the ones to be printed at the end: Initially I tried with adaptive mutation and thought that was the problem, then when mutation_type is left to defaults but the mutation_probability is set, there are duplicates. However, when mutation_probability is set to default, no duplicates are generated. Then, I am not sure how to proceed since I am not sure mutation is happening at all when mutation_type and mutation_probability is set to default. |
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A new release will be pushed soon with a fix to this issue. It happens as crossover creates duplicate genes that, for sometimes , are not solved. |
PyGAD 2.19.0 Release Notes 1. A new `summary()` method is supported to return a Keras-like summary of the PyGAD lifecycle. 2. A new optional parameter called `fitness_batch_size` is supported to calculate the fitness function in batches. If it is assigned the value `1` or `None` (default), then the normal flow is used where the fitness function is called for each individual solution. If the `fitness_batch_size` parameter is assigned a value satisfying this condition `1 < fitness_batch_size <= sol_per_pop`, then the solutions are grouped into batches of size `fitness_batch_size` and the fitness function is called once for each batch. In this case, the fitness function must return a list/tuple/numpy.ndarray with a length equal to the number of solutions passed. #136. 3. The `cloudpickle` library (https://github.com/cloudpipe/cloudpickle) is used instead of the `pickle` library to pickle the `pygad.GA` objects. This solves the issue of having to redefine the functions (e.g. fitness function). The `cloudpickle` library is added as a dependancy in the `requirements.txt` file. #159 4. Support of assigning methods to these parameters: `fitness_func`, `crossover_type`, `mutation_type`, `parent_selection_type`, `on_start`, `on_fitness`, `on_parents`, `on_crossover`, `on_mutation`, `on_generation`, and `on_stop`. #92 #138 5. Validating the output of the parent selection, crossover, and mutation functions. 6. The built-in parent selection operators return the parent's indices as a NumPy array. 7. The outputs of the parent selection, crossover, and mutation operators must be NumPy arrays. 8. Fix an issue when `allow_duplicate_genes=True`. #39 9. Fix an issue creating scatter plots of the solutions' fitness. 10. Sampling from a `set()` is no longer supported in Python 3.11. Instead, sampling happens from a `list()`. Thanks `Marco Brenna` for pointing to this issue. 11. The lifecycle is updated to reflect that the new population's fitness is calculated at the end of the lifecycle not at the beginning. #154 (comment) 12. There was an issue when `save_solutions=True` that causes the fitness function to be called for solutions already explored and have their fitness pre-calculated. #160 13. A new instance attribute named `last_generation_elitism_indices` added to hold the indices of the selected elitism. This attribute helps to re-use the fitness of the elitism instead of calling the fitness function. 14. Fewer calls to the `best_solution()` method which in turns saves some calls to the fitness function. 15. Some updates in the documentation to give more details about the `cal_pop_fitness()` method. #79 (comment)
Hi!
I am trying to solve TSP with GA and it seems like allow_duplicate_genes is not working.
Reproduction:
TSP with 32 citites, each city is represented by number [0, ..., 31]
It gives:
[25 15 20 1 30 1 19 13 29 10 28 3 24 12 12 5 0 26 26 6 7 2 23 16 20 18 8 11 18 3 17 26]
[ 0 1 1 2 3 3 5 6 7 8 10 11 12 12 13 15 16 17 18 18 19 20 20 23 24 25 26 26 26 28 29 30]
As you see numbers 1, 3, 12, 18, 20, 26 are duplicated
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