View source: R/tournamentSelection.R

tournamentSelection | R Documentation |

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.

tournamentSelection(pop, pool_size, tour_size)

`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 |

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

Ching-Shih (Vince) Tsou cstsou@mail.ntcb.edu.tw

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.

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

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