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jade implements JADE: adaptive DE with the current-to-pbest mutation strategy. F and CR are adapted each generation using Cauchy and Normal distributions centered on success histories. An optional external archive stores recently replaced solutions to widen diversity in mutation.

​
Usage

jade(
  NP = 40,
  G = 100,
  data,
  class.name,
  c = 0.1,
  structure = c("nb", "tancl", "tan", "hc"),
  pB = 0.05,
  edgelist = NULL,
  archive = TRUE,
  verbose = 25,
  ...
)

​
Parameters

​
NP
integer
default:"40"
Population size. Must be > 5.
​
G
integer
default:"100"
Maximum generations.
​
data
data.frame
required
Data frame containing features and class variable.
​
class.name
character
required
Name of the class variable column.
​
c
numeric
default:"0.1"
Adaptation rate for updating mean F and CR. Must be in [0, 0.2].
​
structure
character
default:"nb"
BN structure: "nb", "tan", "tancl", or "hc".
​
pB
numeric
default:"0.05"
Fraction of population used as p-best pool for mutation. Must be in (0, 1].
​
edgelist
matrix
Optional custom BN topology (edges × 2 matrix: from, to).
​
archive
logical
default:"TRUE"
If TRUE, replaced individuals are stored in an external archive used in mutation.
​
verbose
integer
default:"25"
Print progress every this many generations.
​
...
Extra arguments passed to bnclassify::tan_cl or bnclassify::tan_hc.

​
Return value

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

​
Example

library(dplbnDE)
data(car)

result <- jade(
  NP = 20, G = 25,
  data = car,
  class.name = names(car)[7],
  c = 0.1, pB = 0.05,
  structure = "hc",
  archive = TRUE,
  verbose = 5,
  k = 3
)
print(result)

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