optimMIES {CEGO}  R Documentation 
Mixed Integer Evolution Strategy (MIES)
Description
An optimization algorithm from the family of Evolution Strategies, designed to optimize mixedinteger problems: The search space is composed of continuous (realvalued) parameters, ordinal integers and categorical parameters. Please note that the categorical parameters need to be coded as integers (type should not be a factor or character). It is an implementation (with a slight modification) of MIES as described by Li et al. (2013). Note, that this algorithm always has a step size for each solution parameter, unlike Li et al., we did not include the option to change to a single stepsize for all parameters. Dominant recombination is used for solution parameters (the search space parameters), intermediate recombination for strategy parameters (i.e., step sizes). Mutation: Selfadaptive, step sizes sigma are optimized alongside the solution parameters. Realvalued parameters are subject to variation based on independent normal distributed random variables. Ordinal integers are subject to variation based on the difference of geometric distributions. Categorical parameters are changed at random, with a selfadapted probability. Note, that a more simple bound constraint method is used. Instead of the Transformation Ta,b(x) described by Li et al., optimMIES simply replaces any value that exceeds the bounds by respective boundary value.
Usage
optimMIES(x = NULL, fun, control = list())
Arguments
x 
Optional start individual(s) as a list. If NULL (default), 
fun 
target function to be minimized. 
control 
(list), with the options:

Details
The control variables types, lower, upper and levels are especially important.
Value
a list:
xbest
best solution found.
ybest
fitness of the best solution.
x
history of all evaluated solutions.
y
corresponding target function values f(x).
count
number of performed target function evaluations.
message
Termination message: Which stopping criterion was reached.
population
Last population.
fitness
Fitness of last population.
References
Rui Li, Michael T. M. Emmerich, Jeroen Eggermont, Thomas Baeck, Martin Schuetz, Jouke Dijkstra, and Johan H. C. Reiber. 2013. Mixed integer evolution strategies for parameter optimization. Evol. Comput. 21, 1 (March 2013), 2964.
See Also
optimCEGO
, optimRS
, optimEA
, optim2Opt
, optimMaxMinDist
Examples
set.seed(1)
controlList < list(lower=c(5,5,1,1,NA,NA),upper=c(10,5,10,10,NA,NA),
types=c("numeric","numeric","integer","integer","factor","factor"),
levels=list(NA,NA,NA,NA,c(1,3,5),1:4),
vectorized = FALSE)
objFun < function(x){
x[[3]] < round(x[[3]])
x[[4]] < round(x[[4]])
y < sum(as.numeric(x[1:4])^2)
if(x[[5]]==1 & x[[6]]==4)
y < exp(y)
else
y < y^2
if(x[[5]]==3)
y<y1
if(x[[5]]==5)
y<y2
if(x[[6]]==1)
y<y*2
if(x[[6]]==2)
y<y * 1.54
if(x[[6]]==3)
y< y +2
if(x[[6]]==4)
y< y * 0.5
if(x[[5]]==1)
y< y * 9
y
}
res < optimMIES(,objFun,controlList)
res$xbest
res$ybest