PBO#

class ioh.iohcpp.problem.PBO#

Bases: IntegerSingleObjective

Pseudo-Boolean Optimization (PBO) problem set.

Contains 25 test functions taking their domain on {0, 1}^n, where n is the length of bitstrings.

In PBO, we cover some theory-motivated function, e.g., OneMax and LeadingOnes as well as others with more practical relevance, e.g., the NK Landscape [DoerrYHWSB20]. We also utilized the so-called W-model for generating/enriching the problem set [WeiseW18].

Reference#

[DoerrYHWSB20] Carola Doerr, Furong Ye, Naama Horesh, Hao Wang, Ofer M. Shir, and Thomas Bäck. “Benchmarking discrete optimization heuristics with IOHprofiler.” Applied Soft Computing 88 (2020): 106027.

[WeiseW18] Thomas Weise and Zijun Wu. “Difficult features of combinatorial optimization problems and the tunable w-model benchmark problem for simulating them.” In Proceedings of the Genetic and Evolutionary Computation Conference Companion, pp. 1769-1776. 2018.

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.PBO

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.PBO

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