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.
create
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	All registered 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__([order, device])	Overloaded function.
add_constraint(self, arg, /)	add a constraint
attach_logger(self, arg, /)	Attach a logger to the problem to allow performance tracking.
detach_logger(self)	Remove the specified logger from the problem.
enforce_bounds(self[, weight, how, exponent])	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
invert(self)
remove_constraint(self, arg, /)	remove a constraint
reset(self)	Reset all state variables of the problem.
set_final_target(self, new_target)	update the final target
set_id(self, new_problem_id)	update the problem id
set_instance(self, new_instance)	update the problem instance
set_name(self, new_name)	update the problem name

Attributes Documentation

bounds

The bounds of the problem.

constraints

The constraints of the problem.

create = <nanobind.nb_func object>

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

All registered 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__(self, arg: ndarray[shape=(*), order='C', device='cpu'], /) → float

__call__(self, arg: collections.abc.Sequence[int], /) → float

__call__(self, arg: ndarray[shape=(*, *), order='C', device='cpu'], /) → numpy.ndarray[dtype=float64, shape=(*)]

__call__(self, arg: collections.abc.Sequence[collections.abc.Sequence[int]], /) → numpy.ndarray[dtype=float64, shape=(*)]

Overloaded function.

1. __call__(self, arg: ndarray[shape=(*), order='C', device='cpu'], /) -> float

2. __call__(self, arg: collections.abc.Sequence[int], /) -> float

   Evaluate the problem.

   x: list

   the search point to evaluate. It must be a 1-dimensional array/list whose length matches search space’s dimensionality

   float

   The evaluated search point

3. __call__(self, arg: ndarray[shape=(*, *), order='C', device='cpu'], /) -> numpy.ndarray[dtype=float64, shape=(*)]

4. __call__(self, arg: collections.abc.Sequence[collections.abc.Sequence[int]], /) -> numpy.ndarray[dtype=float64, shape=(*)]

   Evaluate the problem at several search points.

   x: list[list]

   the search points to evaluate, one per row. It must be a 2-dimensional array/list whose rows’ length matches search space’s dimensionality

   numpy.ndarray

   The objective values of the search points: a new float64 array with one value per row

add_constraint(self, arg: ioh.iohcpp.AbstractIntegerConstraint, /) → None

add a constraint

attach_logger(self, arg: ioh.iohcpp.logger.Logger, /) → None

Attach a logger to the problem to allow performance tracking.

Parameters:

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

detach_logger(self) → None

Remove the specified logger from the problem.

enforce_bounds(self, weight: float = 1.0, how: ioh.iohcpp.ConstraintEnforcement = ConstraintEnforcement.SOFT, exponent: float = 1.0) → None

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

invert(self) → None

remove_constraint(self, arg: ioh.iohcpp.AbstractIntegerConstraint, /) → None

remove a constraint

reset(self) → None

Reset all state variables of the problem.

set_final_target(self, new_target: float) → None

update the final target

set_id(self, new_problem_id: int) → None

update the problem id

set_instance(self, new_instance: int) → None

update the problem instance

set_name(self, new_name: str) → None

update the problem name