metaheuristic_designer.operators.operator_functions.mutation

Mutation operator implementations based on probability distributions.

Functions

mutate_1_sigma(population_matrix, fitness_array)	Mutate a single sigma value using a log-normal update.
mutate_n_sigmas(population_matrix, fitness_array)	Mutate multiple sigma values with global and local learning rates.
mutate_noise(population_matrix, ...[, rng])	Add random noise to N components of each individual.
mutate_sample(population_matrix, ...[, rng])	Replace N components of each individual with random values.
polynomial_mutation(population_matrix, ...)	Polynomial mutation for real-coded genetic algorithms.
rand_noise(population_matrix, fitness_array, ...)	Add random noise to the entire population.
rand_sample(population_matrix, ...[, rng])	Replace the entire population with new random values.
sample_1_sigma(population_matrix, fitness_array)	Replace n components using a log-normal perturbation with a stored sigma value.
xor_mask(population_matrix, fitness_array, N)	Apply bitwise XOR with random masks to N components per individual.