## Installation

The IOHexperimenter is now available on CRAN, and can be installed using:

install.packages('IOHexperimenter')


Alternatively, the development version can be downloaded by either cloning this repository from GitHub and installing locally, or use the following commands to use devtools to install latest version from our GitHub:

If devtools is not yet installed, please first use

install.packages('devtools')


Error messages will be shown in your R console if there is any installation issue. Now, the development version of IOHexperimenter can be installed and loaded using the following commands:

devtools::install_github('IOHprofiler/IOHexperimenter@R')


You can then verify wether the pacakge can be loaded by using:

library('IOHexperimenter')


## Usage

To benchmark your algorithm, you should first create a wrapper around it which accepts an IOHproblem object as its first parameter. This is an S3-object which contains the following information about the current problem:

• dimension
• function_id
• instance
• fopt (if known)
• xopt (if known)
• lower bound
• upper bound
• maximization / minimization
• suite

And the following functions:

• obj_func()
• target_hit()
• set_parameters()

Several example algorithms with corresponding wrappers have been implemented (in the algorithms.R file). These algorithms are (see this page:

• IOH_random_search (can work on functions from either PBO or COCO suites)
• IOH_random_local_search (only for PBO functions)
• IOH_self_adaptive_GA (only for PBO functions)
• IOH_two_rate_GA (only for PBO functions)

Once your algorithm is compatible with an IOHproblem, you can benchmark it using the benchmark_algorithm function, with as the first parameter your (wrapped) algorithm. For information about how to configure this benchmarking procedure, please refer to the internal documentation in R, accesible by using ??benchmark_algorithm.