optimCEGO {CEGO}  R Documentation 
Combinatorial Efficient Global Optimization
Description
Modelbased optimization for combinatorial or mixed problems. Based on measures of distance or dissimilarity.
Usage
optimCEGO(x = NULL, fun, control = list())
Arguments
x 
Optional initial design as a list. If NULL (default), creationFunction (in control list) is used to create initial design.
If x has less individuals than specified by control$evalInit , creationFunction will fill up the design.

fun 
target function to be minimized

control 
(list), with the options of optimization and model building approaches employed:
evalInit Number of initial evaluations (i.e., size of the initial design), integer, default is 2
vectorized Boolean. Defines whether target function is vectorized (takes a list of solutions as argument) or not (takes single solution as argument). Default: FALSE
verbosity Level of text output during run. Defaults to 0, no output.
plotting Plot optimization progress during run (TRUE) or not (FALSE). Default is FALSE.
targetY optimal value to be found, stopping criterion, default is Inf
budget maximum number of target function evaluations, default is 100
creationRetries When a model does not predict an actually improving solution, a random exploration step is performed. creationRetries solutions are created randomly.
For each, distance to all known solutions is calculated. The minimum distance is recorded for each random solution.
The random solution with maximal minimum distance is chosen doe be evaluated in the next iteration.
model Model to be used as a surrogate of the target function. Default is "K" (Kriging). Also
available are: "LM" (linear, distancebased model), "RBFN" Radial Basis Function Network.
modelSettings List of settings for model building, passed on as the control argument to the model training functions modelKriging , modelLinear , modelRBFN .
infill This parameter specifies a function to be used for the infill criterion (e.g., the default is expected improvement infillExpectedImprovement ).
To use no specific infill criterion this has to be set to NA , in which case the prediction of the surrogate model is used. Infill criteria are only used with models that may provide some error estimate with predictions.
optimizer Optimizer that finds the minimum of the surrogate model. Default is optimEA , an Evolutionary Algorithm.
optimizerSettings List of settings (control ) for the optimizer function.
initialDesign Design function that generates the initial design. Default is designMaxMinDist , which creates a design that maximizes the minimum distance between points.
initialDesignSettings List of settings (control ) for the initialDesign function.
creationFunction Function to create individuals/solutions in search space. Default is a function that creates random permutations of length 6
distanceFunction distanceFunction a suitable distance function of type f(x1,x2), returning a scalar distance value, preferably between 0 and 1.
Maximum distances larger 1 are not a problem, but may yield scaling bias when different measures are compared.
Should be nonnegative and symmetric. With the setting control$model="K" this can also be a list of different fitness functions.
Default is Hamming distance for permutations: distancePermutationHamming.

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)
fit
modelfit created in the last iteration
fpred
prediction function created in the last iteration
count
number of performed target function evaluations
message
message string, giving information on termination reason
convergence
error/status code: 1
for termination due
to failed model building, 0
for termination due to depleted budget,
1
if attained objective value is equal to or below target (control$targetY
)
References
Zaefferer, Martin; Stork, Joerg; Friese, Martina; Fischbach, Andreas; Naujoks, Boris; BartzBeielstein, Thomas. (2014). Efficient global optimization for combinatorial problems. In Proceedings of the 2014 conference on Genetic and evolutionary computation (GECCO '14). ACM, New York, NY, USA, 871878. DOI=10.1145/2576768.2598282
Zaefferer, Martin; Stork, Joerg; BartzBeielstein, Thomas. (2014). Distance Measures for Permutations in Combinatorial Efficient Global Optimization. In Parallel Problem Solving from Nature  PPSN XIII (p. 373383). Springer International Publishing.
See Also
modelKriging
, modelLinear
, modelRBFN
, buildModel
, optimEA
Examples
seed < 0
#distance
dF < distancePermutationHamming
#mutation
mF < mutationPermutationSwap
#recombination
rF < recombinationPermutationCycleCrossover
#creation
cF < function()sample(5)
#objective function
lF < landscapeGeneratorUNI(1:5,dF)
#start optimization
set.seed(seed)
res1 < optimCEGO(,lF,list(
creationFunction=cF,
distanceFunction=dF,
optimizerSettings=list(budget=100,popsize=10,
mutationFunction=mF,recombinationFunction=rF),
evalInit=5,budget=15,targetY=0,verbosity=1,model=modelKriging,
vectorized=TRUE)) ##target function is "vectorized", expects list as input
set.seed(seed)
res2 < optimCEGO(,lF,list(
creationFunction=cF,
distanceFunction=dF,
optimizerSettings=list(budget=100,popsize=10,
mutationFunction=mF,recombinationFunction=rF),
evalInit=5,budget=15,targetY=0,verbosity=1,model=modelRBFN,
vectorized=TRUE)) ##target function is "vectorized", expects list as input
res1$xbest
res2$xbest
[Package
CEGO version 2.4.3
Index]