N-ary Crossover Recombinator

Values are chosen componentwise independently at random from multiple individuals. The number of individuals must be determined during construction as n_indivs_in.

The number of output individuals is always 1, i.e. n_indivs_in are used to create one output value. When using this recombinator in a typical EA setting, e.g. with mies_generate_offspring, it is therefore recommended to use a parent-selector where the expected quality of selected parents does not depend on the number of parents selected when n_indivs_in is large: sel("tournament") is preferred to sel("best").

Configuration Parameters

• p :: numeric | matrix
  Sampling weights these are normalized to sum to 1 internally. Must either be a vector of length n_indivs_in, or a matrix with n_indivs_in rows and as many columns as there are components in the values being operated on. Must be non-negative, at least one value per column must be greater than zero, but it is not necessary that they sum to 1.
  Initialized to rep(1, n_indivs_in), i.e. uniform sampling from all individuals being operated on.

Supported Operand Types

Supported Param classes are: ParamDbl

Dictionary

This Recombinator can be created with the short access form rec() (recs() to get a list), or through the the dictionary dict_recombinators in the following way:

# preferred:
rec("convex")
recs("convex")  # takes vector IDs, returns list of Recombinators

# long form:
dict_recombinators$get("convex")

See also

Other recombinators: OperatorCombination, Recombinator, RecombinatorPair, dict_recombinators_cmpmaybe, dict_recombinators_convex, dict_recombinators_cvxpair, dict_recombinators_maybe, dict_recombinators_null, dict_recombinators_proxy, dict_recombinators_sbx, dict_recombinators_sequential, dict_recombinators_swap, dict_recombinators_xounif

Super classes

miesmuschel::MiesOperator -> miesmuschel::Recombinator -> RecombinatorCrossoverNary

Methods

Public methods

• RecombinatorCrossoverNary$new()

• RecombinatorCrossoverNary$clone()

Inherited methods

• miesmuschel::MiesOperator$help()
• miesmuschel::MiesOperator$operate()
• miesmuschel::MiesOperator$prime()
• miesmuschel::MiesOperator$print()
• miesmuschel::MiesOperator$repr()

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Method new()

Initialize the RecombinatorConvex object.

Usage

RecombinatorCrossoverNary$new(n_indivs_in = 2)

Arguments

n_indivs_in

(integer(1))
Number of individuals to consider at the same time. When operating, the number of input individuals must be divisible by this number. Default 2.
The $n_indivs_in field will reflect this value.

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Method clone()

The objects of this class are cloneable with this method.

Usage

RecombinatorCrossoverNary$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

set.seed(1)
rxon = rec("xonary", n_indivs_in = 3)
p = ps(x = p_dbl(-5, 5), y = p_dbl(-5, 5), z = p_dbl(-5, 5))
data = data.frame(x = 0:5, y = 0:5, z = 0:5)

rxon$prime(p)
rxon$operate(data)  # uniform sampling from groups of 3
#>   x y z
#> 1 1 2 2
#> 2 3 4 3

rxon = rec("xonary", 3, p = c(0, 1, 2))$prime(p)
# for groups of 3, take with probability 1/3 from 2nd and with probability 2/3 from 3rd row
rxon$operate(data)
#>   x y z
#> 1 1 2 2
#> 2 5 5 5

pmat = matrix(c(0, 1, 2, 1, 1, 1, 1, 0, 0), ncol = 3)
pmat
#>      [,1] [,2] [,3]
#> [1,]    0    1    1
#> [2,]    1    1    0
#> [3,]    2    1    0

rxon = rec("xonary", 3, p = pmat)$prime(p)
rxon$operate(data)  # componentwise different operation
#>   x y z
#> 1 1 2 0
#> 2 5 3 3