evolvepy.generator.mutation package

Submodules

evolvepy.generator.mutation.binary_mutation module

evolvepy.generator.mutation.binary_mutation.bit_mutation(chromosome: int | float | complex | str | bytes | generic | Sequence[int | float | complex | str | bytes | generic] | Sequence[Sequence[Any]] | _SupportsArray, existence_rate: float, gene_rate: float)[source]
It takes a number n of genes and randomicaly change n gene bits in a chromosome.

If gene_rate = 1 this mutaions behaves like a flipbit mutation, else it is a bitstring mutaion.

Parameters:

  • chromosome (np.ArrayLike) – list of chromosomes of an individual

  • existentce_rate (float) – maximum number of genes modified

  • gene_rate (float) – probability of gene mutation

Returns:

new mutated individual

Return type:

new_cromosome (np.ArrayLike)

evolvepy.generator.mutation.mutation module

class evolvepy.generator.mutation.mutation.BinaryMutationLayer(mutation_function: Callable, existence_rate: float, gene_rate: float, name: str | None = None, chromosome_names: List[str] | str | None = None)[source]

Bases: ChromosomeOperator

Layer destinated to apply the Binary chromosome operations.

__init__(mutation_function: Callable, existence_rate: float, gene_rate: float, name: str | None = None, chromosome_names: List[str] | str | None = None)[source]

Generic caller to a mutation function passed as parameters.

Parameters:

  • mutation_function (class Callable) – Define the function which will be used

  • existence_rate (float) – Probability of first mutation

  • gene_rate (float) – Probability of another gene mutation

  • name (string) – Name for the layer

  • chromosome_names (Union[str, List[str], None]) – Array of chromosomes names (optional)

call_chromosomes(chromosomes: ndarray, fitness: ndarray, context: Context, name: str | None) → ndarray[source]

Apply the mutation on the chromosomes

Parameters:

  • chromosomes (np.ArrayLike) – Array of chromosomes

  • fitness (np.array) – Probability of first mutation

  • context (class Context) – Probability of another gene mutation

  • name (string) – Define the function which will be

Returns:

mutation function

Return type:

BinaryMutationLayer.mutate

static mutate(chromosomes: ndarray, mutation_function: Callable, existence_rate: float, gene_rate: float)[source]

Generic caller to a mutation function passed as parameters.

Parameters:

  • chromosomes (np.ArrayLike) – array of chromosomes

  • existence_rate (float) – probability of first mutation

  • gene_rate (float) – probability of another gene mutation

  • mutation_function (class Callable) – Define the function which will be used

Returns:

return a new mutated population

Return type:

result (np.ArrayLike)

class evolvepy.generator.mutation.mutation.NumericMutationLayer(mutation_function: Callable, existence_rate: float, gene_rate: float, mutation_range: Tuple[float, float], name: str | None = None, chromosome_names: List[str] | str | None = None)[source]

Bases: ChromosomeOperator

Layer destinated to apply the Numeric chromosome operations.

__init__(mutation_function: Callable, existence_rate: float, gene_rate: float, mutation_range: Tuple[float, float], name: str | None = None, chromosome_names: List[str] | str | None = None)[source]

Generic caller to a mutation function passed as parameters.

Parameters:

  • mutation_function (class Callable) – Define the function which will be used

  • existence_rate (float) – Probability of first mutation

  • gene_rate (float) – Probability of another gene mutation

  • name (string) – Name for the layer

  • chromosome_names (Union[str, List[str], None]) – Array of chromosomes names (optional)

call_chromosomes(chromosomes: ndarray, fitness: ndarray, context: Context, name: str | None) → ndarray[source]
static mutate(chromosomes: ndarray, mutation_function: Callable, existence_rate: float, gene_rate: float, mutation_range: Tuple[float, float])[source]

Generic caller to a mutation function passed as parameters.

Parameters:

  • chromosomes (np.ArrayLike) – Array of chromosomes

  • existence_rate (float) – Probability of first mutation

  • gene_rate (float) – Probability of another gene mutation

  • mutation_function (class Callable) – Define the function which will be used

Returns:

return a new mutated population

Return type:

result (np.ArrayLike)

evolvepy.generator.mutation.mutation.default_mutation(type)[source]

evolvepy.generator.mutation.numeric_mutation module

evolvepy.generator.mutation.numeric_mutation.mul_mutation(chromosome: int | float | complex | str | bytes | generic | Sequence[int | float | complex | str | bytes | generic] | Sequence[Sequence[Any]] | _SupportsArray, existence_rate: float, gene_rate: float, mutation_range: Tuple[float, float])[source]

It takes a chromosome and multiply a random value between the mutation range to its gene, then repeats the process with the given probability.

Parameters:

  • chromosome (np.ArrayLike) – array of chromosomes

  • existence_rate (float) – probability of first mutation

  • gene_rate (float) – probability of another gene mutation

  • mutation_range (Tuple[float, float]) –

Returns:

new mutated individual

Return type:

new_cromosome (np.ArrayLike)

evolvepy.generator.mutation.numeric_mutation.sum_mutation(chromosome: int | float | complex | str | bytes | generic | Sequence[int | float | complex | str | bytes | generic] | Sequence[Sequence[Any]] | _SupportsArray, existence_rate: float, gene_rate: float, mutation_range: Tuple[float, float])[source]

It takes a chromosome and add a random value between the mutation range to its gene, then repeats the process with the given probability.

Parameters:

  • chromosome (np.ArrayLike) – array of chromosomes

  • existence_rate (float) – probability of first mutation

  • gene_rate (float) – probability of another gene mutation

  • mutation_range (Tuple[float, float]) –

Returns:

new mutated individual

Return type:

new_cromosome (np.ArrayLike)

Module contents

EvolvePy’s mutations. Objects that can be called upon during evolution to mutate the individual chromosome.