rbga {genalg} | R Documentation |
R Based Genetic Algorithm (floating point chromosome)
Description
A R based genetic algorithm that optimizes, using a user set evaluation function, a set of floats. It takes as input minimum and maximum values for the floats to optimizes. The optimum is the chromosome for which the evaluation value is minimal.
It requires a evalFunc
method to be supplied that takes as argument
the chromosome, a vector of floats.
Additionally, the GA optimization can be monitored by setting a
monitorFunc
that takes a rbga
object as argument.
Results can be visualized with plot.rbga
and summarized with
summary.rbga
.
Usage
rbga(stringMin=c(), stringMax=c(),
suggestions=NULL,
popSize=200, iters=100,
mutationChance=NA,
elitism=NA,
monitorFunc=NULL, evalFunc=NULL,
showSettings=FALSE, verbose=FALSE)
Arguments
stringMin |
vector with minimum values for each gene. |
stringMax |
vector with maximum values for each gene. |
suggestions |
optional list of suggested chromosomes |
popSize |
the population size. |
iters |
the number of iterations. |
mutationChance |
the chance that a gene in the chromosome mutates. By default 1/(size+1). It affects the convergence rate and the probing of search space: a low chance results in quicker convergence, while a high chance increases the span of the search space. |
elitism |
the number of chromosomes that are kept into the next generation. By default is about 20% of the population size. |
monitorFunc |
Method run after each generation to allow monitoring of the optimization |
evalFunc |
User supplied method to calculate the evaluation function for the given chromosome |
showSettings |
if true the settings will be printed to screen. By default False. |
verbose |
if true the algorithm will be more verbose. By default False. |
References
C.B. Lucasius and G. Kateman (1993). Understanding and using genetic algorithms - Part 1. Concepts, properties and context. Chemometrics and Intelligent Laboratory Systems 19:1-33.
C.B. Lucasius and G. Kateman (1994). Understanding and using genetic algorithms - Part 2. Representation, configuration and hybridization. Chemometrics and Intelligent Laboratory Systems 25:99-145.
See Also
Examples
# optimize two values to match pi and sqrt(50)
evaluate <- function(string=c()) {
returnVal = NA;
if (length(string) == 2) {
returnVal = abs(string[1]-pi) + abs(string[2]-sqrt(50));
} else {
stop("Expecting a chromosome of length 2!");
}
returnVal
}
monitor <- function(obj) {
# plot the population
xlim = c(obj$stringMin[1], obj$stringMax[1]);
ylim = c(obj$stringMin[2], obj$stringMax[2]);
plot(obj$population, xlim=xlim, ylim=ylim,
xlab="pi", ylab="sqrt(50)");
}
rbga.results = rbga(c(1, 1), c(5, 10), monitorFunc=monitor,
evalFunc=evaluate, verbose=TRUE, mutationChance=0.01)
plot(rbga.results)
plot(rbga.results, type="hist")
plot(rbga.results, type="vars")