ribs.emitters.GeneticAlgorithmEmitter¶

class ribs.emitters.GeneticAlgorithmEmitter(archive, *, x0=None, initial_solutions=None, bounds=None, batch_size=64, operator_kwargs=None, operator=None)[source]¶

Emits solutions by using operator provided.

If the archive is empty and self._initial_solutions is set, a call to ask() will return self._initial_solutions. If self._initial_solutions is not set, we operate on self.x0.

Parameters

archive (ribs.archives.ArchiveBase) – An archive to use when creating and inserting solutions. For instance, this can be ribs.archives.GridArchive.
x0 (numpy.ndarray) – Initial solution.
operator (str) – Internal Operator Class used to Mutate Solutions in ask method.
operator_kwargs (dict) – Additional arguments to pass to the operator. See ribs.emitters.operators for the arguments allowed by each operator.
initial_solutions (array-like) – An (n, solution_dim) array of solutions to be used when the archive is empty. If this argument is None, then solutions will be sampled from a Gaussian distribution centered at x0 with standard deviation sigma.
bounds (None or array-like) – Bounds of the solution space. Solutions are clipped to these bounds. Pass None to indicate there are no bounds. Alternatively, pass an array-like to specify the bounds for each dim. Each element in this array-like can be None to indicate no bound, or a tuple of (lower_bound, upper_bound), where lower_bound or upper_bound may be None to indicate no bound.
batch_size (int) – Number of solutions to return in ask().

Raises

ValueError – There is an error in x0 or initial_solutions.
ValueError – There is an error in the bounds configuration.

Methods

`ask`()	Creates solutions with operator provided.
`ask_dqd`()	Generates a `(batch_size, solution_dim)` array of solutions for which gradient information must be computed.
`tell`(solution, objective, measures, ...)	Gives the emitter results from evaluating solutions.
`tell_dqd`(solution, objective, measures, ...)	Gives the emitter results from evaluating the gradient of the solutions, only used for DQD emitters.

Attributes

`archive`	The archive which stores solutions generated by this emitter.
`batch_size`	Number of solutions to return in `ask()`.
`initial_solutions`	The initial solutions which are returned when the archive is empty (if x0 is not set).
`lower_bounds`	`(solution_dim,)` array with lower bounds of solution space.
`solution_dim`	The dimension of solutions produced by this emitter.
`upper_bounds`	`(solution_dim,)` array with upper bounds of solution space.
`x0`	Initial Solution (if initial_solutions is not set).

ask()[source]¶

Creates solutions with operator provided.

If the archive is empty and self._initial_solutions is set, we return self._initial_solutions. If self._initial_solutions is not set and the archive is still empty, we operate on the initial solution (x0) provided. Otherwise, we sample parents from the archive to be used as input to the operator

Returns: If the archive is not empty, (batch_size, solution_dim) array – contains batch_size new solutions to evaluate. If the archive is empty, we return self._initial_solutions, which might not have batch_size solutions.
Return type: numpy.ndarray

ask_dqd()¶

Generates a (batch_size, solution_dim) array of solutions for which gradient information must be computed.

This method only needs to be implemented by emitters used in DQD. The method returns an empty array by default.

tell(solution, objective, measures, add_info, **fields)¶

Gives the emitter results from evaluating solutions.

This base class implementation (in EmitterBase) needs to be overriden.

Parameters

solution (numpy.ndarray) – Array of solutions generated by this emitter’s ask() method.
objective (numpy.ndarray) – 1D array containing the objective function value of each solution.
measures (numpy.ndarray) – (n, <measure space dimension>) array with the measure space coordinates of each solution.
add_info (dict) – Data returned from the archive add() method.
fields (keyword arguments) – Additional data for each solution. Each argument should be an array with batch_size as the first dimension.

tell_dqd(solution, objective, measures, jacobian, add_info, **fields)¶

Gives the emitter results from evaluating the gradient of the solutions, only used for DQD emitters.

Parameters

solution (numpy.ndarray) – (batch_size, :attr:`solution_dim`) array of solutions generated by this emitter’s ask() method.
objective (numpy.ndarray) – 1-dimensional array containing the objective function value of each solution.
measures (numpy.ndarray) – (batch_size, measure space dimension) array with the measure space coordinates of each solution.
jacobian (numpy.ndarray) – (batch_size, 1 + measure_dim, solution_dim) array consisting of Jacobian matrices of the solutions obtained from ask_dqd(). Each matrix should consist of the objective gradient of the solution followed by the measure gradients.
add_info (dict) – Data returned from the archive add() method.
fields (keyword arguments) – Additional data for each solution. Each argument should be an array with batch_size as the first dimension.