Package 'IOHexperimenter'

S3 generic as.character function for IOHexperimenter

Description

S3 generic as.character function for IOHexperimenter

Usage

## S3 method for class 'IOHexperimenter'
as.character(x, ...)

Arguments

x	The IOHexperimenter to print
...	Arguments for underlying function

Examples

as.character(IOHexperimenter())

---

S3 generic as.character function for IOHproblem

Description

S3 generic as.character function for IOHproblem

Usage

## S3 method for class 'IOHproblem'
as.character(x, ...)

Arguments

x	The IOHproblem to print
...	Arguments for underlying function

Examples

exp <- IOHexperimenter()
p <- next_problem(exp)
as.character(p)

---

Base procedure for benchmarking a custom algorithm

Description

Base procedure for benchmarking a custom algorithm

Usage

benchmark_algorithm(user_alg, suite = "PBO", functions = c(1, 2),
  instances = c(1, 2), dimensions = 16, data.dir = NULL,
  algorithm.info = " ", algorithm.name = " ", params.track = NULL,
  repetitions = 5)

Arguments

user_alg	Function defining the custom algorithm. Needs to accept one paramter: an IOHproblem object, which has the following properties: dimension function_id instance suite (Currently 'BBOB' or 'PBO') And the following functions: obj_func target_hit set_parameters
suite	Which suite to test on
functions	Which function to test on
instances	Which instances to test on
dimensions	Which dimensions to test on
data.dir	Where the data should be stored (defaults to "./data" when not provided)
algorithm.info	Additional information about the algorithm you plan on running
algorithm.name	The name of the algorithm you plan on running
params.track	Which parameters to track. Should be a vector of strings, containing no spaces or commas
repetitions	How many independent runs of the algorithm to do for each problem instance

Examples

benchmark_algorithm(IOH_two_rate_GA, params.track = 'Mutation_rate', data.dir = './data')

---

IOHexperimenter-based wrapper

Description

For easier use with the IOHexperimenter

The simplest stochastic optimization algorithm for discrete problems. A randomly chosen position in the solution vector is perturbated in each iteration. Only improvements are accepted after perturbation.

Usage

IOH_random_local_search(IOHproblem, budget = NULL)

random_local_search(dimension, obj_func, target_hit = function() {    
  FALSE }, budget = NULL)

Arguments

IOHproblem	An IOHproblem object
budget	integer, maximal allowable number of function evaluations
dimension	Dimension of search space
obj_func	The evaluation function
target_hit	Optional, function which enables early stopping if a target value is reached

Examples

benchmark_algorithm(IOH_random_local_search, data.dir = NULL)

---

IOHexperimenter-based wrapper

Description

For easier use with the IOHexperimenter

Random walk in ${0, 1}^d$ space; Maximization

Random walk in continuous space;

Usage

IOH_random_search(IOHproblem, budget = NULL)

random_search_PB(dim, obj_func, target_hit = function() {     FALSE },
  budget = NULL)

random_search(dim, obj_func, target_hit = function() {     FALSE },
  budget = NULL, lbound = -1, ubound = 1, maximize = T)

Arguments

IOHproblem	An IOHproblem object
budget	Integer, maximal allowable number of function evaluations
dim	Dimension of search space
obj_func	The evaluation function
target_hit	Optional, function which enables early stopping if a target value is reached
lbound	Lower bound of search space. Either single number or vector of size 'dim'
ubound	Upper bound of search space. Either single number or vector of size 'dim'
maximize	Whether to perform maximization or minimization. The function assumes minimization, achieved by inverting the obj_func when 'maximize' is FALSE

Examples

benchmark_algorithm(IOH_random_search, data.dir = NULL)

---

IOHexperimenter-based wrapper

Description

For easier use with the IOHexperimenter

A genetic algorithm that controls the mutation rate (strength) using the so-called self-adaptation mechanism: the mutation rate is firstly perturbated and then the resulting value is taken to mutate Lambda solution vector. The best solution is selected along with its mutation rate.

Usage

IOH_self_adaptive_GA(IOHproblem, lambda_ = 1, budget = NULL)

self_adaptive_GA(dimension, obj_func, lambda_ = 10, budget = NULL,
  set_parameters = NULL, target_hit = function() {     FALSE })

Arguments

IOHproblem	An IOHproblem object
lambda_	The size of the offspring
budget	How many times the objective function can be evaluated
dimension	Dimension of search space
obj_func	The evaluation function
set_parameters	Function to call to store the value of the registered parameters
target_hit	Optional, function which enables early stopping if a target value is reached

Examples

one_comma_two_EA <- function(IOHproblem) { IOH_self_adaptive_GA(IOHproblem, lambda_=2) }

benchmark_algorithm(one_comma_two_EA, params.track = "Mutation_rate",
algorithm.name = "one_comma_two_EA", data.dir = NULL,
algorithm.info = "Using one_comma_two_EA with specific parameter" )

---

IOHexperimenter-based wrapper

Description

For easier use with the IOHexperimenter

A genetic algorithm that controls the mutation rate (strength) using the so-called 2-rate self-adaptation mechanism: the mutation rate is based on a parameter r. For each generation, half offspring are generated by mutation rate 2r/dim, and half by r/2dim. r that the best offspring has been created with will be inherited by probability 3/4, the other by 1/4.

Usage

IOH_two_rate_GA(IOHproblem, lambda_ = 1, budget = NULL)

two_rate_GA(dimension, obj_func, target_hit = function() {     FALSE },
  lambda_ = 2, budget = NULL, set_parameters = NULL)

Arguments

IOHproblem	An IOHproblem object
lambda_	The size of the offspring
budget	How many times the objective function can be evaluated
dimension	Dimension of search space
obj_func	The evaluation function
target_hit	Optional, function which enables early stopping if a target value is reached
set_parameters	Function to call to store the value of the registered parameters

Examples

benchmark_algorithm(IOH_two_rate_GA)

---

S3 class 'IOHexperimenter'

Description

S3 class 'IOHexperimenter'

Usage

IOHexperimenter(suite = "PBO", dims = NULL, functions = NULL,
  instances = NULL, algorithm.info = " ", algorithm.name = " ",
  data.dir = NULL, param.track = NULL)

Arguments

suite	Which suite to use. Available: 'PBO', 'BBOB'
dims	Numerical Which dimensions to use
functions	Numerical Which functions from the selected suite to use
instances	Numerical Which problem instances to use
algorithm.info	Additional information about the algorithm you plan on running
algorithm.name	The name of the algorithm you plan on running
data.dir	Where the data should be stored. Defaults to NULL, meaning no data is stored.
param.track	Which parameters to track. Should be a vector of strings, containing no spaces or commas

Value

A S3 object 'DataSet'

Examples

exp <- IOHexperimenter()

---

get an IOHproblem objects

Description

If an 'experimenter'-argument is provided, this is the same function as 'next_problem' If not, this creates a suite consisting of a single function based on the other arguments

Usage

IOHproblem(suite = "PBO", functionnr = 1, dim = 16, instance = 1,
  experimenter = NULL)

Arguments

suite	The suite to use. Either 'PBO' or 'BBOB'
functionnr	The number of the function to create
dim	The dimension of the function to create
instance	The instance of the function to create
experimenter	(optional) an IOHexperimenter object

Details

An IOHproblem-object has the following attributes:

• "Dimension": The dimesion of the problem

• "function_id: The number of the function

• "instance: The number of the function-instance

• suite: The suite of the function. Either 'PBO' or 'BBOB'

• fopt: If known, the optimal value of the function

• lbound: The lower bound of the searchspace

• ubound: The upper bound of the searchspace

• maximization: Boolean indicating whether the function should be maximized or minimized

• params.track: The parameters which are being tracked on this function. Only available if initialized in the underlying IOHexperimenter-object (or when using the 'benchmark_algorithm'-function)

In addition to these attributes, there are three function-attributes available to use:

• obj_function: The interface to evaluate the function

• target_hit: Boolean indicating if the optimal has been hit (if known)

• set_parameters: Interface to storing the current parameter values (if param.track is initialized), This has two arguments: the list of names of parameters to update (must match those of param.track) and a list of equal length containing their respective values.

Value

An IOHproblem object

Examples

p <- IOHproblem()

---

Get the next function of the currently initialized IOHexperimenter object

Description

Get the next function of the currently initialized IOHexperimenter object

Usage

next_problem(experimenter)

Arguments

experimenter

The IOHexperimenter object

Value

An IOHproblem object if available, NULL otherwise

Examples

exp <- IOHexperimenter()
p <- next_problem(exp)

---

S3 print function for IOHexperimenter

Description

S3 print function for IOHexperimenter

Usage

## S3 method for class 'IOHexperimenter'
print(x, ...)

Arguments

x	The IOHexperimenter to print
...	Arguments for underlying function

Examples

print(IOHexperimenter())

---

S3 print function for IOHproblem

Description

S3 print function for IOHproblem

Usage

## S3 method for class 'IOHproblem'
print(x, ...)

Arguments

x	The IOHproblem to print
...	Arguments for underlying function

Examples

exp <- IOHexperimenter()
p <- next_problem(exp)
print(p)

---

Reset the IOHproblem

Description

Reset the IOHproblem

Usage

reset_problem(problem)

Arguments

problem

The IOHproblem object

Value

An IOHproblem object

Examples

exp <- IOHexperimenter()
p <- next_problem(exp)
IOH_random_search(p)
p <- reset_problem(p)

---

S3 generic summary operator for IOHexperimenter

Description

S3 generic summary operator for IOHexperimenter

Usage

## S3 method for class 'IOHexperimenter'
summary(object, ...)

Arguments

object	A IOHexperimenter object
...	Arguments passed to other methods

Value

A summary of the IOHexperimenter object.

Examples

summary(IOHexperimenter())

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