# This benchmark script is based on one written by Dirk Eddelbuettel
# while he was porting DEoptim to Rcpp. His efforts in code review
# and benchmarking are greatly appreciated.
demo.DEbenchmark <- function() {
Rosenbrock <- function(x){
x1 <- x[1]
x2 <- x[2]
100 * (x2 - x1 * x1)^2 + (1 - x1)^2
}
Wild <- function(x) { ## 'Wild' function, global minimum at about -15.81515
sum(10 * sin(0.3 * x) * sin(1.3 * x^2) + 0.00001 * x^4 + 0.2 * x + 80)/length(x)
}
Rastrigin <- function(x) {
sum(x+2 - 10 * cos(2*pi*x)) + 20
}
Genrose <- function(x) { ## One generalization of the Rosenbrock banana valley function (n parameters)
n <- length(x)
1.0 + sum (100 * (x[-n]^2 - x[-1])^2 + (x[-1] - 1)^2)
}
maxIt <- 250 # not excessive but so that we get some run-time on simple problems
basicDE <- function(n, maxIt, fun) DEoptim(fn=fun, lower=rep(-25, n), upper=rep(25, n),
control=list(NP=10*n, itermax=maxIt, trace=FALSE))#, bs=TRUE))
adaptDE <- function(n, maxIt, fun) DEoptim(fn=fun, lower=rep(-25, n), upper=rep(25, n),
control=list(NP=10*n, itermax=maxIt, trace=FALSE, strategy=6))#, bs=TRUE))
runPair <- function(n, maxIt, fun) {
gc()
set.seed(42)
bt <- mean(replicate(10, system.time(invisible(basicDE(n, maxIt, fun)))[3]), trim=0.1)
gc()
set.seed(42)
ct <- mean(replicate(10, system.time(invisible(adaptDE(n, maxIt, fun)))[3]), trim=0.1)
return(data.frame(DE=bt, JADE=ct))
}
cat("# At", format(Sys.time()), "\n")
reps <- c(5, 10, 20)
res <- rbind(
do.call(rbind, lapply(reps, runPair, maxIt, function(...) Rosenbrock(...))),
do.call(rbind, lapply(reps, runPair, maxIt, function(...) Rastrigin(...))),
do.call(rbind, lapply(reps, runPair, maxIt, function(...) Wild(...))),
do.call(rbind, lapply(reps, runPair, maxIt, function(...) Genrose(...)))
)
res <- rbind(res, colMeans(res))
rownames(res) <- c( paste("Rosenbrock", reps, sep=""),
paste("Rastrigin", reps, sep=""),
paste("Wild", reps, sep=""),
paste("Genrose", reps, sep=""),
"MEANS")
res
}
demo.DEbenchmark()
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