Description
Usage
Arguments
Value
See Also
View source: R/setMBOControlMultiPoint.R
Extends an MBO control object with options for multipoint proposal.
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13  (
control,
method = ,
cl.lie = ,
moimbo.objective = ,
moimbo.dist = ,
moimbo.selection = ,
moimbo.maxit = ,
moimbo.sbx.eta = ,
moimbo.sbx.p = ,
moimbo.pm.eta = ,
moimbo.pm.p =
)

control 
[MBOControl ]
Control object for mbo.

method 
[character(1) ]
Method used for proposal of multiple infill points, for parallel batch evaluation.
Possible values are:
“cb”: Proposes points by optimizing the confidence bound “cb” criterion,
propose.points times. Each lambda value for “cb” is drawn randomly from an
exp(1)distribution, so do not define infill.opt.cb.lambda .
The optimizer for each proposal is configured in the same way as for the single point case,
i. e., by specifying infill.opt and related stuff.
“moimbo”: Proposes points by multiobjective infill criteria via evolutionary multiobjective optimization.
The EA is a (mu+1) type of algorithm and runs for moimbo.maxit generations.
The population size is set to propose.points .
The selection criterion is moimbo.selection .
If this method is selected the infill criterion in setMBOInfillCrit is ignored.
“cl”: Proposes points by constant liar strategy.
Only meaningful if infill.crit == "cb"
In the first step the kriging model is fitted based on the real data and the best point is calculated
according to the regular EIcriterion.
Then, the function value of the best point is simply guessed by the worst seen function evaluation.
This lie is used to update the model in order to propose the subsequent point.
The procedure is applied until the number of best points achieves propose.points .
Default is cb .

cl.lie 
[function ]
Function used by constant liar method for lying. Default is min .

moimbo.objective 
[character(1) ]
Objectives which are optimized in multiobjective approach.
Possible values are: “mean.dist”, “ei.dist”, “mean.se”, “mean.se.dist”.
Default is “ei.dist”.

moimbo.dist 
[character(1) ]
Distance function used in multiobjective EA.
Possible values are: “nearest.neighbor”, “nearest.better”.
Default is “nearest.better”.

moimbo.selection 
[character(1) ]
Method used for selecting 1 element for removal from the population
in each iteration of the multiobjective EA.
Possible values are:
“hypervolume”: Nondominated sorting + hypervolume contribution.
“crowdingdist”: Nondominated sorting + crowding distance based ranking.
“first”: Nondominated sorting + first objective of moimbo.objective as criterion.
“last”: Nondominated sorting + last objective of moimbo.objective as criterion.
Default is hypervolume .

moimbo.maxit 
[integer(1) ]
Number of generations for multiobjective EA.
Default is 100.

moimbo.sbx.eta 
[numeric(1) ]
Distance parameter of crossover distribution, see sbx_operator .
Default is 15.

moimbo.sbx.p 
[numeric(1) ]
Probability of 1point crossover, see sbx_operator .
Default is 1.

moimbo.pm.eta 
[numeric(1) ]
Distance parameter of mutation distribution, see pm_operator .
Default is 15.

moimbo.pm.p 
[numeric(1) ]
Probability of 1point mutation, see pm_operator .
Default is 1.

[MBOControl
].
Other MBOControl:
makeMBOControl()
,
setMBOControlInfill()
,
setMBOControlMultiObj()
,
setMBOControlTermination()
mlrMBO documentation built on Jan. 13, 2021, 7:57 a.m.