metaheuristic_designer.encodings.special.self_adapting_ES_encoding module

Encoding for self-adapting Evolution Strategies that appends mutation strength parameters.

class SelfAdaptingESEncoding(dimension, single_sigma=True, base_encoding=None)

Bases: ParameterExtendingEncoding

Encoding for self-adapting Evolution Strategies.

Appends one or more mutation strength values (F) to the solution vector. When single_sigma=True a single step size is shared by all dimensions; otherwise each dimension gets its own step size.

Parameters:

dimensionint

Number of decision variables.

single_sigmabool, optional

If True (default), a single F value is added. If False, dimension values are added.

base_encodingEncoding, optional

Encoding applied to the solution part. Defaults to DefaultEncoding.

Attributes:

params

Access parameter values by attribute-style lookup.

Parameters:

• dimension (int)

• single_sigma (bool)

• base_encoding (Optional[Encoding])

Methods

decode(population_matrix)	Decodes a population matrix into a list/array of solutions.
decode_params(genotype[, copy])	Extract the auxiliary parameter blocks from a genotype matrix.
encode(solution[, params])	Encodes a list of solutions to our problem to an population matrix.
encode_params(param_dict)	Stack a dictionary of parameter arrays into a single matrix.
extract_params(population_matrix)	Return only the auxiliary-parameter part of the genotype matrix.
extract_solution(population_matrix)	Return only the solution part of the genotype 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