R: Set multi-objective options.

setMBOControlMultiObj {mlrMBO}

R Documentation

Set multi-objective options.

Description

Extends MBO control object with multi-objective specific options.

Usage

setMBOControlMultiObj(
  control,
  method = NULL,
  ref.point.method = NULL,
  ref.point.offset = NULL,
  ref.point.val = NULL,
  parego.s = NULL,
  parego.rho = NULL,
  parego.use.margin.points = NULL,
  parego.sample.more.weights = NULL,
  parego.normalize = NULL,
  dib.indicator = NULL,
  mspot.select.crit = NULL
)

Arguments

`control`	[`MBOControl`] Control object for mbo.
`method`	[`character(1)`] Which multi-objective method should be used? “parego”: The ParEGO algorithm. “dib”: Direct indicator-based method. Subsumes SMS-EGO and epsilon-EGO. “mspot”: Directly optimizes multicrit problem where we substitute the true objectives with model-based infill crits via an EMOA. All methods can also propose multiple points in parallel. Default is “dib”.
`ref.point.method`	[`character(1)`] Method for the determination of the reference point used for S-metric. Currently used for “mspot” and “dib” with indicator “sms”. Possible Values are: “all”: In each dimension: maximum of all points + `ref.point.offset`. “front”: In each dimension: maximum of all non-dominated points + `ref.point.offset` “const”: Constant value, see `ref.point.val`. Default is “all”.
`ref.point.offset`	[`numeric(1)`] See `ref.point.method`, default is 1.
`ref.point.val`	[`numeric`] Constant value of reference point for hypervolume calculation. Used if `ref.point.method = "const"`. Has to be specified in this case.
`parego.s`	[`integer(1)`] Parameter of parego - controls the number of weighting vectors. The default depends on `n.objectives` and leads to ca. 100000 different possible weight vectors. The defaults for (2, 3, 4, 5, 6) dimensions are (100000, 450, 75, 37, 23) and 10 for higher dimensions.
`parego.rho`	[`numeric(1)`] Parameter of parego - factor for Tchebycheff function. Default 0.05 as suggested in parego paper.
`parego.use.margin.points`	[`logical`] For each target function: Should the weight vector (0, ..., 0, 1, 0, ..., 0), i.e. the weight vector with only 0 and a single 1 at the i-th position for the i-th target function, be drawn with probability 1? Number of TRUE entries must be less or equal to `propose.points` Default is not to do this.
`parego.sample.more.weights`	[`integer(1)`] In each iteration `parego.sample.more.weights` * `propose.points` are sampled and the weights with maximum distance to each other are chosen. Default is 1, if only 1 point is proposed each iteration, otherwise 5.
`parego.normalize`	[`character`] Normalization to use. Either map the whole image space to [0, 1] (`standard`, the default) or just the paretofront (`front`).
`dib.indicator`	[`character(1)`] Either “sms” (SMS-EGO like algorithm) or “eps” (epsilon-EGO like algorithm). Default is “sms”.
`mspot.select.crit`	[`MBOInfillCrit`] Which infill.crit to use in the candidate selection. After the NSGA2 proposed a set of candidates, “propose.points” are selected via the hypervolume contribution of this infill.crit. Possible values are “crit.mr” and “crit.cb” (or any other InfillCrit generated with `makeMBOInfillCritCB`), default is “crit.mr”.

Value

[MBOControl].

References

For more information on the implemented multi-objective procedures the following sources might be helpful: Knowles, J.: ParEGO: A hybrid algorithm with on-line landscape approximation for expensive multiobjective optimization problems. IEEE Transactions on Evolutionary Computation, 10 (2006) 1, pp. 50-66

Wagner, T.; Emmerich, M.; Deutz, A.; Ponweiser, W.: On Expected- Improvement Criteria for Model-Based Multi-Objective Optimization. In: Proc. 11th Int. Conf. Parallel Problem Solving From Nature (PPSN XI) - Part I, Krakow, Poland, Schaefer, R.; Cotta, C.; Kolodziej, J.; Rudolph, G. (eds.), no. 6238 in Lecture Notes in Computer Science, Springer, Berlin, 2010, ISBN 978-3-642-15843-8, pp. 718-727, doi:10. 1007/978-3-642-15844-5 72

Wagner, T.: Planning and Multi-Objective Optimization of Manufacturing Processes by Means of Empirical Surrogate Models. No. 71 in Schriftenreihe des ISF, Vulkan Verlag, Essen, 2013, ISBN 978-3-8027-8775-1

Zaefferer, M.; Bartz-Beielstein, T.; Naujoks, B.; Wagner, T.; Emmerich, M.: A Case Study on Multi-Criteria Optimization of an Event Detection Software under Limited Budgets. In: Proc. 7th International. Conf. Evolutionary Multi-Criterion Optimization (EMO 2013), March 19-22, Sheffield, UK, R. Purshouse; P. J. Fleming; C. M. Fonseca; S. Greco; J. Shaw, eds., 2013, vol. 7811 of Lecture Notes in Computer Science, ISBN 978-3-642-37139-4, pp. 756770, doi:10.1007/978-3-642-37140-0 56