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Additive random mutation with gene_space low and high #229
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My default approach to this would be to use rejection sampling and Gaussian noise, but I'm not sure how well it aligns with the methods currently used for additive random mutations in PyGAD, which seem to rely on uniform sampling from a fixed range.
I suggest:
- Add new parameter,
mutation_noise_variance - Generate
noise ~ N(0, 1) - Set
candidate_value = old_value + mutation_noise_variance*noise - Set
new_value = candidate_valueiflow < candidate_value < highelse repeat from 2
Let me know if you'd be happy with this and I'll submit a PR.
Example code:
def mutate(offspring: np.ndarray, ga_instance: pygad.GA) -> np.ndarray: """Apply random Gaussian noise to a proportion of genes in the offspring. Parameters ---------- offspring : np.ndarray Array of shape (sol_per_pop, num_genes) containing the offspring. ga_instance : pygad.GA Instance of the GA class """ # Convert the bounds to numpy arrays lower_bounds = np.tile( np.array([b["low"] for b in ga_instance.gene_space]), (offspring.shape[0], 1) ) upper_bounds = np.tile( np.array([b["high"] for g in ga_instance.gene_space]), (offspring.shape[0], 1) ) # Select the genes to mutate # This generates a boolean array of the same shape as offspring, with True for each gene that will be mutated genes_to_mutate = rng.random(offspring.shape) < ga_instance.mutation_probability print("Total genes to mutate: ", np.sum(genes_to_mutate)) # Generate random values to add to the selected genes # Use rejection sampling to ensure that the mutated genes remain within the bounds remaining_genes_to_mutate = genes_to_mutate[:] while np.any(remaining_genes_to_mutate): print("Remaining genes to mutate: ", np.sum(remaining_genes_to_mutate)) # Generate candidate mutated values # This generates an array of floats with the same shape as offspring candidate_values = rng.normal( loc=offspring, scale=ga_instance.mutation_noise_variance, size=offspring.shape ) # Accept only the candidates that are within the bounds candidate_is_valid = (candidate_values >= lower_bounds) & ( candidate_values <= upper_bounds ) # Accept only the candidates that are yet to be accepted candidate_is_valid &= remaining_genes_to_mutate # Update the remaining genes to mutate remaining_genes_to_mutate &= ~candidate_is_valid # Update the offspring array offspring = np.where(candidate_is_valid, candidate_values, offspring) return offspring ```
- Add new parameter,
You are right. But this should be supported without adding additional parameters. The 2 parameters:
random_mutation_min_valrandom_mutation_max_val
should be used to generate the random value.
In the docs, it says that:
However, if a
gene_spaceis defined as a dict withlowandhighparameters, it should be possible to generate additive mutations, so thatnew_value = old_value + random_value, rather thannew_value = random_value.I had a brief look in the source code, and this wasn't supported at the moment.