tournamentSelection: Tournament Selection

In nsga2R: Elitist Non-Dominated Sorting Genetic Algorithm

Tournament Selection

Description

Tournaments are played among several solutions. The best one is chosen according to their nondomination levels and crowding distances. And it is placed in the mating pool.

Usage

tournamentSelection(pop, pool_size, tour_size)

Arguments

pop	Population matrix with nondomination rank and crowding distance
pool_size	Size of mating pool, usually same as the population size
tour_size	Size of tournament, the selection pressure can be adjusted by varying the tournament size

Value

Return the mating pool with decision variables, objective functions, nondomination level, and crowding distance

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

library(mco)
tourSize <- popSize <- 10
lowerBounds <- rep(0,30)
upperBounds <- rep(1,30)
varNo <- length(lowerBounds)
objDim <- 2
set.seed(1234)
population <- t(sapply(1:popSize, function(u) array(runif(length(lowerBounds),
  lowerBounds,upperBounds))))
population <- cbind(population, t(apply(population,1,zdt3)))
ranking <- fastNonDominatedSorting(population[,(varNo+1):(varNo+objDim)])
rnkIndex <- integer(popSize)
i <- 1
while (i <= length(ranking)) {
  rnkIndex[ranking[[i]]] <- i
  i <- i + 1
} 
population <- cbind(population,rnkIndex);
objRange <- apply(population[,(varNo+1):(varNo+objDim)], 2, max) -
  apply(population[,(varNo+1):(varNo+objDim)], 2, min);
cd <- crowdingDist4frnt(population,ranking,objRange)
population <- cbind(population,apply(cd,1,sum))
matingPool <- tournamentSelection(population,popSize,tourSize)
matingPool

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