SBOX#

class ioh.iohcpp.problem.SBOX#

Bases: RealSingleObjective

Black-Box Optimization Benchmarking (BBOB) problem set.

Contains 24 noiselessreal-valued test functions supported on [-5, 5]^n, where n is the dimensionality.

This problem was orginally proposed by Hansen et. al. in [FinckHRA10] and was implemented as the core component of the COmparing Continous Optimizer (COCO) platform [HansenARMTB20].

We took the implementation of those 24 functions in https://github.com/numbbo/coco/tree/master/code-experiments/src (v2.2) and adopted those to our framework.

We have acknowledged and specified in our license file https://github.com/IOHprofiler/IOHexperimenter/blob/master/LICENSE.md the usage and modification to the COCO/BBOB sources.

Reference#

[HansenARMTB20] Nikolaus Hansen, Anne Auger, Raymond Ros, Olaf Mersmann, Tea Tusar, and Dimo Brockhoff. “COCO: A platform for comparing continuous optimizers in a black-box setting.” Optimization Methods and Software (2020): 1-31.

[FinckHRA10] Steffen Finck, Nikolaus Hansen, Raymond Ros, and Anne Auger. “Real-parameter black-box optimization benchmarking 2009: Presentation of the noiseless functions.” Technical Report 2009/20, Research Center PPE, 2009. Updated February, 2010.

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`(args, *kwargs)	Overloaded function.
`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

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 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]

add_constraint()#: add a constraint

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(*args, **kwargs)#

Overloaded function.

create(problem_name: str, instance_id: int, dimension: int) -> ioh.iohcpp.problem.SBOX

Create a problem instance

problem_name: str
a string indicating the problem name.

instance_id: int
an integer identifier of the problem instance

dimension: int
the dimensionality of the search space
create(problem_id: int, instance_id: int, dimension: int) -> ioh.iohcpp.problem.SBOX

Create a problem instance

problem_name: int
a string indicating the problem name.

instance_id: int
an integer identifier of the problem instance

dimension: int
the dimensionality of the search space

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