metaheuristic_designer.encoding module#

Base class for the Encoding module.

This module implements a way to have a different representation in the inner working of the algorithm and the result of the procedure.

class Encoding(decode_as_array=False, name=None, **kwargs)[source]#

Bases: ParametrizableMixin, ABC

Translate between internal genotypes and problem-specific phenotypes.

An Encoding is responsible for converting a population matrix (the internal representation used by operators) into a collection of solutions that the objective function understands, and vice versa.

Parameters:

decode_as_arraybool, optional: If True, decode() returns a NumPy array instead of an iterable of arbitrary objects. Default False.
namestr, optional: Display name for this encoding.
**kwargs: Additional keyword arguments stored as schedulable parameters.

Attributes:

params: Access parameter values by attribute-style lookup.

Parameters:

decode_as_array (bool)

Methods

`decode`(population)	Decodes a population matrix into a list/array of solutions.
`encode`(solutions)	Encodes a list of solutions to our problem to an population matrix.
`gather_params`()	Overridable thin wrapper around get_params
`get_params`()	Return a copy of the current parameter dictionary.
`store_kwargs`([progress])	Store keyword arguments and evaluate them at the given progress.
`update`(progress)	Re-evaluate all stored parameters at the current progress.
`update_kwargs`([progress])	Add or replace parameters and immediately evaluate them.

get_state

gather_params()[source]#

Overridable thin wrapper around get_params

Return type:: dict

abstract encode(solutions)[source]#

Encodes a list of solutions to our problem to an population matrix.

Return type:

ndarray[tuple[int, int], floating] | ndarray[tuple[int, int], integer] | ndarray[tuple[int, int], uint8 | bool]

Parameters:

solutions: Iterable: Solutions that should be encoded.

Returns:

population: ndarray: Population array.

Parameters:

solutions (Iterable)

abstract decode(population)[source]#

Decodes a population matrix into a list/array of solutions.

Return type:

Iterable

Parameters:

population: ndarray: Population that should be decoded.

Returns:

solutions: Iterable: List/array of solutions.

Parameters:

population (ndarray[tuple[int, int], floating] | ndarray[tuple[int, int], integer] | ndarray[tuple[int, int], uint8 | bool])

get_state()[source]#

Return type:: dict

class DefaultEncoding(decode_as_array=True)[source]#

Bases: Encoding

Identity encoding - the internal genotype is used directly.

No transformation is applied; encode() and decode() return their arguments unchanged. This is the encoding used when no other is specified.

Parameters:

decode_as_arraybool, optional: See Encoding. Default True.

Attributes:

params: Access parameter values by attribute-style lookup.

Parameters:

decode_as_array (bool)

Methods

`decode`(population)	Decodes a population matrix into a list/array of solutions.
`encode`(solution)	Encodes a list of solutions to our problem to an population matrix.
`gather_params`()	Overridable thin wrapper around get_params
`get_params`()	Return a copy of the current parameter dictionary.
`store_kwargs`([progress])	Store keyword arguments and evaluate them at the given progress.
`update`(progress)	Re-evaluate all stored parameters at the current progress.
`update_kwargs`([progress])	Add or replace parameters and immediately evaluate them.

get_state

encode(solution)[source]#

Encodes a list of solutions to our problem to an population matrix.

Return type:

ndarray[tuple[int, int], floating] | ndarray[tuple[int, int], integer] | ndarray[tuple[int, int], uint8 | bool]

Parameters:

solutions: Iterable: Solutions that should be encoded.

Returns:

population: ndarray: Population array.

Parameters:

solution (Iterable)

decode(population)[source]#

Decodes a population matrix into a list/array of solutions.

Return type:

Iterable

Parameters:

population: ndarray: Population that should be decoded.

Returns:

solutions: Iterable: List/array of solutions.

Parameters:

population (ndarray[tuple[int, int], floating] | ndarray[tuple[int, int], integer] | ndarray[tuple[int, int], uint8 | bool])

class EncodingFromLambda(encode_fn, decode_fn, **kwargs)[source]#

Bases: Encoding

Encoding built from two callables.

Parameters:

encode_fncallable: (solutions) -> population_matrix.
decode_fncallable: (population_matrix) -> solutions.
decode_as_arraybool, optional: See Encoding.
**kwargs: Forwarded to Encoding.

Attributes:

params: Access parameter values by attribute-style lookup.

Parameters:

encode_fn (Callable)
decode_fn (Callable)

Methods

`decode`(population)	Decodes a population matrix into a list/array of solutions.
`encode`(solution)	Encodes a list of solutions to our problem to an population matrix.
`gather_params`()	Overridable thin wrapper around get_params
`get_params`()	Return a copy of the current parameter dictionary.
`store_kwargs`([progress])	Store keyword arguments and evaluate them at the given progress.
`update`(progress)	Re-evaluate all stored parameters at the current progress.
`update_kwargs`([progress])	Add or replace parameters and immediately evaluate them.

get_state

encode(solution)[source]#

Encodes a list of solutions to our problem to an population matrix.

Return type:

ndarray[tuple[int, int], floating] | ndarray[tuple[int, int], integer] | ndarray[tuple[int, int], uint8 | bool]

Parameters:

solutions: Iterable: Solutions that should be encoded.

Returns:

population: ndarray: Population array.

Parameters:

solution (Iterable)

decode(population)[source]#

Decodes a population matrix into a list/array of solutions.

Return type:

Iterable

Parameters:

population: ndarray: Population that should be decoded.

Returns:

solutions: Iterable: List/array of solutions.

Parameters:

population (ndarray[tuple[int, int], floating] | ndarray[tuple[int, int], integer] | ndarray[tuple[int, int], uint8 | bool])