CEGO (version 2.0.0)

optimEA: Evolutionary Algorithm for Combinatorial Optimization

Description

A basic implementation of a simple Evolutionary Algorithm for Combinatorial Optimization. Default evolutionary operators aim at permutation optimization problems.

Usage

optimEA(x = NULL, fun, control = list())

Arguments

x

Optional start individual(s) as a list. If NULL (default), creationFunction (in control list) is used to create initial design. If x has less individuals than the population size, creationFunction will fill up the r

fun

target function to be minimized

control

(list), with the options budget The limit on number of target function evaluations (stopping criterion) (default: 1000) popsize Population size (default: 100) generations Number of generations (stopping criterion) (d

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

See Also

optimCEGO, optimRS, optim2Opt

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)
res <- optimEA(,lF,list(creationFunction=cF,mutationFunction=mF,recombinationFunction=rF,
		popsize=15,budget=100,targetY=0,verbosity=1,
		vectorized=TRUE)) ##target function is "vectorized", expects list as input
res$xbest