GARS_Elitism: Separate chromosome on the basis of the Fitness Scores

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

Description

This function splits the chromosome population in two parts allowing the best chromosomes to be preserved from the "evolutionary" steps: Selection, Crossover and Mutation.

Usage

GARS_Elitism(chr.pop, fitn.values, n.elit = 10)

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
fitn.values	A numeric vector where each element corresponds to the fitness score of each chromosome in 'chr.pop'
n.elit	The number of best chromosomes to be selected by elitism. This number must be even. Default is 10

Value

A list containing:

• The population of best chromosomes selected by elitism.

• The population of chromosomes not selected by elitism.

• The fitness values of best chromosomes selected by elitism.

• The fitness values of chromosomes not selected by elitism.

Author(s)

Mattia Chiesa, Luca Piacentini

See Also

GARS_Mutation, GARS_Selection, GARS_Crossover, GARS_FitFun,

Examples

data(GARS_popul)
data(GARS_Fitness_score)
pop_list <- GARS_Elitism(GARS_popul, GARS_Fitness_score)

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