optimGenoud: Minimization by GENetic Optimization Using Derivatives

In SPOT: Sequential Parameter Optimization Toolbox

Minimization by GENetic Optimization Using Derivatives

Description

For minimization, this function uses the "genoud" method from the codergenoud package. It is basically a wrapper, to enable genoud for usage in SPOT.

Usage

optimGenoud(x = NULL, fun, lower, upper, control = list(), ...)

Arguments

x	optional start point, not used
fun	objective function, which receives a matrix x and returns observations y
lower	boundary of the search space
upper	boundary of the search space
control	list of control parameters funEvals Budget, number of function evaluations allowed. Default is 100. populationSize Population size, number of individuals in the population. Default is 10*dimension.
...	passed to fun

Value

list, with elements

x

NULL, currently not used

y

NULL, currently not used

xbest

best solution

ybest

best observation

count

number of evaluations of fun

Examples

res <- optimGenoud(,fun = funSphere,lower = c(-10,-20),upper=c(20,8))
res$ybest

SPOT documentation built on June 26, 2022, 1:06 a.m.