generationalDistance {mco} | R Documentation |
Functions to evaulate the quality of the estimated pareto front.
generationalDistance(x, o) generalizedSpread(x, o) dominatedHypervolume(x, ref)
x |
Estimated pareto front or an object which has a paretoFront method |
o |
True pareto front or an object which has a paretoFront method |
ref |
Reference point (may be omitted). |
Instead of the pareto front, one can also pass an object for which a paretoFront method exists to both methods.
For dominatedHypervolume
, if no reference point is given, the
maximum in each dimension is used as the reference point.
The respective quality measure.
Heike Trautmann trautmann@statistik.uni-dortmund.de, Detlef Steuer steuer@hsu-hamburg.de and Olaf Mersmann olafm@statistik.uni-dortmund.de
Carlos M. Fonseca, Luis Paquete, and Manuel Lopez-Ibanez. An improved dimension-sweep algorithm for the hypervolume indicator. In IEEE Congress on Evolutionary Computation, pages 1157-1163, Vancouver, Canada, July 2006.
sch1 <- function(x) c(x^2, (x-2)^2) ## Estimate true front: tf <- nsga2(sch1, 1, 2, lower.bounds=0, upper.bounds=1, popsize=1000, generations=20) res <- nsga2(sch1, 1, 2, lower.bounds=0, upper.bounds=1, popsize=16, generations=c(2, 4, 6, 8, 10, 20, 50)) n <- length(res) sapply(1:n, function(i) generationalDistance(res[[i]], tf)) sapply(1:n, function(i) generalizedSpread(res[[i]], tf))