mlr_acqfunctions_smsego {mlr3mbo}

Acquisition Function SMS-EGO

Description

S-Metric Selection Evolutionary Multi-Objective Optimization Algorithm Acquisition Function.

Parameters

• "lambda" (numeric(1))
  \lambda value used for the confidence bound. Defaults to 1. Based on confidence = (1 - 2 * dnorm(lambda)) ^ m you can calculate a lambda for a given confidence level, see Ponweiser et al. (2008).

• "epsilon" (numeric(1))
  \epsilon used for the additive epsilon dominance. Can either be a single numeric value > 0 or NULL (default). In the case of being NULL, an epsilon vector is maintained dynamically as described in Horn et al. (2015).

Super classes

bbotk::Objective -> mlr3mbo::AcqFunction -> AcqFunctionSmsEgo

Public fields

ys_front

(matrix())
Approximated Pareto front. Signs are corrected with respect to assuming minimization of objectives.

ref_point

(numeric())
Reference point. Signs are corrected with respect to assuming minimization of objectives.

epsilon

(numeric())
Epsilon used for the additive epsilon dominance.

progress

(numeric(1))
Optimization progress (typically, the number of function evaluations left). Note that this requires the bbotk::OptimInstance to be terminated via a bbotk::TerminatorEvals.

Methods

Public methods

• AcqFunctionSmsEgo$new()

• AcqFunctionSmsEgo$update()

• AcqFunctionSmsEgo$clone()

Inherited methods

• bbotk::Objective$eval()
• bbotk::Objective$format()
• bbotk::Objective$help()
• bbotk::Objective$print()
• mlr3mbo::AcqFunction$eval_dt()
• mlr3mbo::AcqFunction$eval_many()

Method new()

Creates a new instance of this R6 class.

Usage

AcqFunctionSmsEgo$new(surrogate = NULL, lambda = 1, epsilon = NULL)

Arguments

surrogate

(NULL | SurrogateLearnerCollection).

lambda

(numeric(1)).

epsilon

(NULL | numeric(1)).

Method update()

Updates acquisition function and sets ys_front, ref_point, epsilon.

Usage

AcqFunctionSmsEgo$update()

Method clone()

The objects of this class are cloneable with this method.

Usage

AcqFunctionSmsEgo$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

References

• Ponweiser, Wolfgang, Wagner, Tobias, Biermann, Dirk, Vincze, Markus (2008). “Multiobjective Optimization on a Limited Budget of Evaluations Using Model-Assisted S-Metric Selection.” In Proceedings of the 10th International Conference on Parallel Problem Solving from Nature, 784–794.

• Horn, Daniel, Wagner, Tobias, Biermann, Dirk, Weihs, Claus, Bischl, Bernd (2015). “Model-Based Multi-objective Optimization: Taxonomy, Multi-Point Proposal, Toolbox and Benchmark.” In International Conference on Evolutionary Multi-Criterion Optimization, 64–78.

See Also

Other Acquisition Function: AcqFunction, mlr_acqfunctions, mlr_acqfunctions_aei, mlr_acqfunctions_cb, mlr_acqfunctions_ehvi, mlr_acqfunctions_ehvigh, mlr_acqfunctions_ei, mlr_acqfunctions_eips, mlr_acqfunctions_mean, mlr_acqfunctions_pi, mlr_acqfunctions_sd

Examples

if (requireNamespace("mlr3learners") &
    requireNamespace("DiceKriging") &
    requireNamespace("rgenoud")) {
  library(bbotk)
  library(paradox)
  library(mlr3learners)
  library(data.table)

  fun = function(xs) {
    list(y1 = xs$x^2, y2 = (xs$x - 2) ^ 2)
  }
  domain = ps(x = p_dbl(lower = -10, upper = 10))
  codomain = ps(y1 = p_dbl(tags = "minimize"), y2 = p_dbl(tags = "minimize"))
  objective = ObjectiveRFun$new(fun = fun, domain = domain, codomain = codomain)

  instance = OptimInstanceBatchMultiCrit$new(
    objective = objective,
    terminator = trm("evals", n_evals = 5))

  instance$eval_batch(data.table(x = c(-6, -5, 3, 9)))

  learner = default_gp()

  surrogate = srlrn(list(learner, learner$clone(deep = TRUE)), archive = instance$archive)

  acq_function = acqf("smsego", surrogate = surrogate)

  acq_function$surrogate$update()
  acq_function$progress = 5 - 4 # n_evals = 5 and 4 points already evaluated
  acq_function$update()
  acq_function$eval_dt(data.table(x = c(-1, 0, 1)))
}