RealSingleObjective#

class ioh.iohcpp.problem.RealSingleObjective#

Bases: pybind11_object

Base class for RealSingleObjective problems

Attributes Summary

`bounds`	The bounds of the problem.
`constraints`	The constraints of the problem.
`log_info`	The data is that being sent to the logger.
`meta_data`	The static meta-data of the problem containing, e.g., problem id, instance id, and problem's dimensionality
`optimum`	The optimum and its objective value for a problem instance
`problems`
`state`	The current state of the optimization process containing, e.g., the current solution and the number of function evaluated consumed so far

Methods Summary

`__call__`	Evaluate the problem.
`add_constraint`	add a constraint
`attach_logger`	Attach a logger to the problem to allow performance tracking.
`create`	Create a problem instance
`detach_logger`	Remove the specified logger from the problem.
`enforce_bounds`	Enforced the bounds (box-constraints) as constraint :param weight: :type weight: The weight for computing the penalty (can be infinity to have strict box-constraints) :param how: :type how: The enforcement strategy, should be one of the 'ioh.ConstraintEnforcement' options :param exponent: :type exponent: The exponent for scaling the contraint
`remove_constraint`	remove a constraint
`reset`	Reset all state variables of the problem.
`set_id`	update the problem id
`set_instance`	update the problem instance
`set_name`	update the problem name

Attributes Documentation

meta_data#: The static meta-data of the problem containing, e.g., problem id, instance id, and problem’s dimensionality

state#: The current state of the optimization process containing, e.g., the current solution and the number of function evaluated consumed so far

Methods Documentation

__call__()#

Evaluate the problem.

Parameters:: x (list) – the search point to evaluate. It must be a 1-dimensional array/list whose length matches search space’s dimensionality
Returns:: The evaluated search point
Return type:: float

Evaluate the problem.

Parameters:: x (list[list]) – the search points to evaluate. It must be a 2-dimensional array/list whose length matches search space’s dimensionality
Returns:: The evaluated search points
Return type:: list[float]

attach_logger()#

Attach a logger to the problem to allow performance tracking.

Parameters:: logger (Logger) – A logger-object from the IOHexperimenter logger module.

static create()#

Create a problem instance

Parameters:

problem_name (a string indicating the problem name.) –
instance_id (an integer identifier of the problem instance, which seeds the random generation) – of the tranformations in the search/objective spaces
dimension (integer, representing the dimensionality of the search space) –

Create a problem instance

Parameters:

problem_id (the index of the problem to create.) –
instance_id (an integer identifier of the problem instance, which seeds the random generation) – of the tranformations in the search/objective spaces
dimension (integer, representing the dimensionality of the search space) –

enforce_bounds()#: Enforced the bounds (box-constraints) as constraint :param weight: :type weight: The weight for computing the penalty (can be infinity to have strict box-constraints) :param how: :type how: The enforcement strategy, should be one of the ‘ioh.ConstraintEnforcement’ options :param exponent: :type exponent: The exponent for scaling the contraint