Run Optimizers Sequentially

OptimizerBatchChain allows to run multiple OptimizerBatch sequentially.

For each OptimizerBatch an (optional) additional Terminator can be specified during construction. While the original Terminator of the OptimInstanceBatch guards the optimization process as a whole, the additional Terminators guard each individual OptimizerBatch.

The optimization process works as follows: The first OptimizerBatch is run on the OptimInstanceBatch relying on a TerminatorCombo of the original Terminator of the OptimInstanceBatch and the (optional) additional Terminator as passed during construction. Once this TerminatorCombo indicates termination (usually via the additional Terminator), the second OptimizerBatch is run. This continues for all optimizers unless the original Terminator of the OptimInstanceBatch indicates termination.

OptimizerBatchChain can also be used for random restarts of the same Optimizer (if applicable) by setting the Terminator of the OptimInstanceBatch to TerminatorNone and setting identical additional Terminators during construction.

Dictionary

This Optimizer can be instantiated via the dictionary mlr_optimizers or with the associated sugar function opt():

mlr_optimizers$get("chain")
opt("chain")

Parameters

Parameters are inherited from the individual OptimizerBatch and collected as a paradox::ParamSetCollection (with set_ids potentially postfixed via _1, _2, ..., if the same OptimizerBatch are used multiple times).

Progress Bars

$optimize() supports progress bars via the package progressr combined with a Terminator. Simply wrap the function in progressr::with_progress() to enable them. We recommend to use package progress as backend; enable with progressr::handlers("progress").

Super classes

bbotk::Optimizer -> bbotk::OptimizerBatch -> OptimizerBatchChain

Methods

Public methods

• OptimizerBatchChain$new()

• OptimizerBatchChain$clone()

Inherited methods

• bbotk::Optimizer$format()
• bbotk::Optimizer$help()
• bbotk::Optimizer$print()
• bbotk::OptimizerBatch$optimize()

---

Method new()

Creates a new instance of this R6 class.

Usage

OptimizerBatchChain$new(
  optimizers,
  terminators = rep(list(NULL), length(optimizers))
)

Arguments

optimizers

(list of Optimizers).

terminators

(list of Terminators | NULL).

---

Method clone()

The objects of this class are cloneable with this method.

Usage

OptimizerBatchChain$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

library(paradox)

domain = ps(x = p_dbl(lower = -1, upper = 1))

search_space = ps(x = p_dbl(lower = -1, upper = 1))

codomain = ps(y = p_dbl(tags = "minimize"))

objective_function = function(xs) {
 list(y = as.numeric(xs)^2)
}

objective = ObjectiveRFun$new(
 fun = objective_function,
 domain = domain,
 codomain = codomain
)

terminator = trm("evals", n_evals = 10)

# run optimizers sequentially
instance = OptimInstanceBatchSingleCrit$new(
 objective = objective,
 search_space = search_space,
 terminator = terminator
)

optimizer = opt("chain",
 optimizers = list(opt("random_search"), opt("grid_search")),
 terminators = list(trm("evals", n_evals = 5), trm("evals", n_evals = 5))
)

optimizer$optimize(instance)
#>             x  x_domain          y
#>         <num>    <list>      <num>
#> 1: 0.08496082 <list[1]> 0.00721834

# random restarts
instance = OptimInstanceBatchSingleCrit$new(
 objective = objective,
 search_space = search_space,
 terminator = trm("none")
)
optimizer = opt("chain",
 optimizers = list(opt("gensa"), opt("gensa")),
 terminators = list(trm("evals", n_evals = 10), trm("evals", n_evals = 10))
)
optimizer$optimize(instance)
#>                x  x_domain            y
#>            <num>    <list>        <num>
#> 1: -3.266831e-13 <list[1]> 1.067219e-25