CEGO (version 2.0.0)

optimCEGO: Combinatorial Efficient Global Optimization

Description

Model-based 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, creationFun

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 tar

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 model-fit 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; Bartz-Beielstein, 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, 871-878. DOI=10.1145/2576768.2598282 http://doi.acm.org/10.1145/2576768.2598282 Zaefferer, Martin; Stork, Joerg; Bartz-Beielstein, Thomas. (2014). Distance Measures for Permutations in Combinatorial Efficient Global Optimization. In Parallel Problem Solving from Nature - PPSN XIII (p. 373-383). Springer International Publishing.

See Also

modelKriging, modelLinear, modelRBFN, buildModel, optimEA

Examples

seed <- 0
glgseed=1
#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