The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.

bbotk - Black-Box Optimization Toolkit

Package website: release | dev

bbotk is a black-box optimization framework for R. It features highly configurable search spaces via the paradox package and optimizes every user-defined objective function. The package includes several optimization algorithms e.g. Random Search, Grid Search, Iterated Racing, Bayesian Optimization (in mlr3mbo) and Hyperband (in mlr3hyperband). bbotk is the base package of mlr3tuning, mlr3fselect and miesmuschel.

Resources

There are several sections about black-box optimization in the mlr3book. Often the sections about tuning are also relevant for general black-box optimization.

Getting started with black-box optimization.
An overview of all optimizers and tuners can be found on our website.
Learn about log transformations in the search space.
Or more advanced search space transformations.
Run multi-objective optimization.
The mlr3viz package can be used to visualize the optimization process.
Quick optimization with the bb_optimize function.

Installation

Install the latest release from CRAN.

install.packages("bbotk")

Install the development version from GitHub.

pak::pkg_install("mlr-org/bbotk")

Example

# define the objective function
fun = function(xs) {
  - (xs[[1]] - 2)^2 - (xs[[2]] + 3)^2 + 10
}

# set domain
domain = ps(
  x1 = p_dbl(-10, 10),
  x2 = p_dbl(-5, 5)
)

# set codomain
codomain = ps(
  y = p_dbl(tags = "maximize")
)

# create objective
objective = ObjectiveRFun$new(
  fun = fun,
  domain = domain,
  codomain = codomain,
  properties = "deterministic"
)

# initialize instance
instance = oi(
  objective = objective,
  terminator = trm("evals", n_evals = 20)
)

# load optimizer
optimizer = opt("gensa")

# trigger optimization
optimizer$optimize(instance)

##    x1 x2  x_domain  y
## 1:  2 -3 <list[2]> 10

# best performing configuration
instance$result

##    x1 x2  x_domain  y
## 1:  2 -3 <list[2]> 10

# all evaluated configuration
as.data.table(instance$archive)

##            x1        x2          y           timestamp batch_nr x_domain_x1 x_domain_x2
##  1: -4.689827 -1.278761 -37.716445 2024-08-13 17:52:54        1   -4.689827   -1.278761
##  2: -5.930364 -4.400474 -54.851999 2024-08-13 17:52:54        2   -5.930364   -4.400474
##  3:  7.170817 -1.519948 -18.927907 2024-08-13 17:52:54        3    7.170817   -1.519948
##  4:  2.045200 -1.519948   7.807403 2024-08-13 17:52:54        4    2.045200   -1.519948
##  5:  2.045200 -2.064742   9.123250 2024-08-13 17:52:54        5    2.045200   -2.064742
## ---                                                                                    
## 16:  2.000000 -3.000000  10.000000 2024-08-13 17:52:54       16    2.000000   -3.000000
## 17:  2.000001 -3.000000  10.000000 2024-08-13 17:52:54       17    2.000001   -3.000000
## 18:  1.999999 -3.000000  10.000000 2024-08-13 17:52:54       18    1.999999   -3.000000
## 19:  2.000000 -2.999999  10.000000 2024-08-13 17:52:54       19    2.000000   -2.999999
## 20:  2.000000 -3.000001  10.000000 2024-08-13 17:52:54       20    2.000000   -3.000001

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.