GARS_Crossover: Perform the one-point and the two-point Crossover

In BioinfoMonzino/GARS: GARS: Genetic Algorithm for the identification of Robust Subsets of variables in high-dimensional and challenging datasets

Description

This function implements the one-point and the two-point cross-over.

Usage

GARS_Crossover(chr.pop, co.rate = 0.8, type = c("one.p", "two.p"),
  one.p.quart = c("I.quart", "II.quart", "III.quart"))

Arguments

chr.pop	A matrix or a data.frame representing the chromosomes population: each column is a chromosome and each element corresponds to the feature position in the data matrix
co.rate	The probability of each random couple of chromosomes to swap some parts. It must be between 0 and 1. Default is 0.8
type	The type of crossover method; one-point ("one.p") and two-point ("two.p") are allowed. Default is "one.p"
one.p.quart	The position of the cromosome where performing the crossover, if "one.p" is selected. The first quartile ("I.quart"), the second quartile ("II.quart", i.e. the median) and the third quartile ("III.quart") are allowed. Default is "I.quart"

Value

A matrix representing the "crossed" population. The dimensions of this matrix are the same of 'chr.pop'

Author(s)

Mattia Chiesa, Luca Piacentini

See Also

GARS_Mutation, GARS_Selection, GARS_Elitism,

Examples

data(GARS_popul)
crossed_pop <- GARS_Crossover(GARS_popul, co.rate=0.9)
crossed_pop <- GARS_Crossover(GARS_popul, type="two.p")
crossed_pop <- GARS_Crossover(GARS_popul, type="one.p",
one.p.quart= "II.quart")

BioinfoMonzino/GARS documentation built on Aug. 2, 2021, 1:08 a.m.