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Documentation Index

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mos implements Multiple Offspring Sampling: a framework that dynamically combines SHADE (global exploration) and Solis-Wets local search (intensification). The participation of each technique is adjusted each split based on their recent improvement quality.

​
Usage

mos(
  NP = 40,
  G = 100,
  data,
  class.name,
  structure = c("nb", "tancl", "tan", "hc"),
  pB = 0.05,
  edgelist = NULL,
  n_splits = 10,
  min_participation = 0.1,
  verbose = 25,
  ...
)

​
Parameters

​
NP
integer
default:"40"
Population size.
​
G
integer
default:"100"
Maximum generations.
​
data
data.frame
required
Training data frame.
​
class.name
character
required
Class variable column name.
​
structure
character
default:"nb"
BN structure: "nb", "tan", "tancl", "hc".
​
pB
numeric
default:"0.05"
Fraction of best individuals for SHADE mutation.
​
edgelist
matrix
Optional custom BN topology (edges × 2 matrix).
​
n_splits
integer
default:"10"
Number of budget partitions. G is divided into n_splits blocks.
​
min_participation
numeric
default:"0.1"
Minimum participation fraction for either technique (prevents one from being eliminated).
​
verbose
integer
default:"25"
Print progress each split.
​
...
Extra arguments for bnclassify::tan_cl or bnclassify::tan_hc.

​
Return value

Object of class DE: Best, BestCLL, pobFinal, CLLPobFinal, N.evals, convergence, evaluations.

​
Example

library(dplbnDE)
data(car)

result <- mos(
  NP = 20, G = 25,
  data = car,
  class.name = names(car)[7],
  n_splits = 5,
  verbose = 5
)
print(result)