boundedSBXover: Bounded Simulated Binary Crossover Operator

In nsga2R: Elitist Non-Dominated Sorting Genetic Algorithm

Bounded Simulated Binary Crossover Operator

Description

The simulated binary crossover operator is a real-parameter genetic operator. It simulates the working principal of the single-point crossover operator on binary strings.

Usage

boundedSBXover(parent_chromosome, lowerBounds, upperBounds, cprob, mu)

Arguments

parent_chromosome	Mating pool with decision variables
lowerBounds	Lower bounds of each decision variable
upperBounds	Upper bounds of each decision variable
cprob	Crossover probability
mu	Crossover distribution index, it can be any nonnegative real number

Value

Return the offspring population with decision variables

Author(s)

Ching-Shih (Vince) Tsou [e-mail]

References

Deb, K., Pratap, A., Agarwal, S., and Meyarivan, T. (2002), " A fast and elitist multiobjective genetic algorithm: NSGA-II", IEEE Transactions on Evolutionary Computation, 6(2), 182-197.

Examples

set.seed(1234)
lowerBounds <- rep(0,30)
upperBounds <- rep(1,30)
cprob <- 0.7
XoverDistIdx <- 20
matingPool <- matrix(runif(1200, 0, 1), nrow=40, ncol=30)
childAfterX <- boundedSBXover(matingPool,lowerBounds,upperBounds,cprob,XoverDistIdx)
childAfterX

nsga2R documentation built on May 23, 2022, 5:06 p.m.