optimES: Evolution Strategy

In SPOT: Sequential Parameter Optimization Toolbox

Evolution Strategy

Description

This is an implementation of an Evolution Strategy.

Usage

optimES(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	is a vector that defines the lower boundary of search space (this also defines the dimensionality of the problem)
upper	is a vector that defines the upper boundary of search space (same length as lower)
control	list of control parameters. The control list can contain the following settings: funEvals number of function evaluations, stopping criterion, default is 500 mue number of parents, default is 10 nu selection pressure. That means, number of offspring (lambda) is mue multiplied with nu. Default is 10 mutation string of mutation type, default is 1 sigmaInit initial sigma value (step size), default is 1.0 nSigma number of different sigmas, default is 1 tau0 number, default is 0.0. tau0 is the general multiplier. tau number, learning parameter for self adaption, i.e. the local multiplier for step sizes (for each dimension).default is 1.0 rho number of parents involved in the procreation of an offspring (mixing number), default is "bi" sel number of selected individuals, default is 1 stratReco Recombination operator for strategy variables. 1: none. 2: dominant/discrete (default). 3: intermediate. 4: variation of intermediate recombination. objReco Recombination operator for object variables. 1: none. 2: dominant/discrete (default). 3: intermediate. 4: variation of intermediate recombination. maxGen number of generations, stopping criterion, default is Inf seed number, random seed, default is 1 noise number, value of noise added to fitness values, default is 0.0 verbosity defines output verbosity of the ES, default is 0 plotResult boolean, specifies if results are plotted, default is FALSE logPlotResult boolean, defines if plot results should be logarithmic, default is FALSE sigmaRestart number, value of sigma on restart, default is 0.1 preScanMult initial population size is multiplied by this number for a pre-scan, default is 1 globalOpt termination criterion on reaching a desired optimum value, default is rep(0,dimension)
...	additional parameters to be passed on 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

cont <- list(funEvals=100)
optimES(fun=funSphere,lower=rep(0,2), upper=rep(1,2), control= cont) 

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