% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/eaf-package.R
\docType{package}
\name{eaf-package}
\alias{eaf-package}
\alias{_PACKAGE}
\alias{eaf}
\title{Plots of the Empirical Attainment Function}
\description{
The empirical attainment function (EAF) describes the probabilistic
distribution of the outcomes obtained by a stochastic algorithm in the
objective space. This package implements plots of summary
attainment surfaces and differences between the first-order
EAFs. These plots may be used for exploring the performance of
stochastic local search algorithms for biobjective optimization
problems and help in identifying certain algorithmic behaviors in a
graphical way.
}
\details{
Functions:

\tabular{rl}{
\code{\link[=eafdiffplot]{eafdiffplot()}} \tab  Empirical attainment function differences\cr
\code{\link[=eafplot]{eafplot()}} \tab  Plot the Empirical Attainment Function for two objectives\cr
\code{\link[=read_datasets]{read_datasets()}} \tab  Read several data.frame sets
}

Data:

\describe{
\item{\code{\link{gcp2x2}}}{  Metaheuristics for solving the Graph Vertex Coloring Problem}
\item{\code{\link{HybridGA}}}{  Results of Hybrid GA on vanzyl and Richmond
water networks}
\item{\code{\link{SPEA2minstoptimeRichmond}}}{ Results of SPEA2 when minimising electrical cost and maximising the
minimum idle time of pumps on Richmond water network}
}

Extras are available at \code{system.file(package="eaf")}:

\tabular{rl}{
\code{extdata}        \tab  External data sets (see \code{\link{read_datasets}}) \cr
\code{scripts/eaf}    \tab  EAF command-line program \cr
\code{scripts/eafplot}\tab  Perl script to generate plots of attainment surfaces\cr
\code{scripts/eafdiff}\tab  Perl script to generate plots of EAF differences
}
}
\examples{
data(gcp2x2)
tabucol<-subset(gcp2x2, alg!="TSinN1")
tabucol$alg<-tabucol$alg[drop=TRUE]
eafplot(time+best~run,data=tabucol,subset=tabucol$inst=="DSJC500.5")

eafplot(time+best~run|inst,groups=alg,data=gcp2x2)
eafplot(time+best~run|inst,groups=alg,data=gcp2x2,
percentiles = c(0,50,100), cex = 1.4, lty = c(2,1,2),lwd = c(2,2,2),
       col = c("black","blue","grey50"))
extdata_path <- system.file(package="eaf","extdata")
A1 <- read_datasets(file.path(extdata_path,"ALG_1_dat"))
A2 <- read_datasets(file.path(extdata_path,"ALG_2_dat"))
eafplot(A1, percentiles=c(50))
eafplot(list(A1=A1, A2=A2), percentiles=c(50))
eafdiffplot(A1, A2)
## Save to a PDF file
# dev.copy2pdf(file="eaf.pdf", onefile=TRUE, width=5, height=4)
}
\references{
V. Grunert da Fonseca, C. M. Fonseca, and A. O. Hall, \emph{Inferential
performance assessment of stochastic optimisers and the attainment
function}, in Evolutionary Multi-Criterion Optimization. First
International Conference, EMO 2001 (E. Zitzler, K. Deb, L. Thiele,
C. A. Coello Coello, and D. Corne, eds.), vol. 1993 of Lecture Notes
in Computer Science, pp. 213-225, Berlin: Springer, 2001.

V. Grunert da Fonseca and C. M. Fonseca, \emph{The attainment-function
approach to stochastic multiobjective optimizer assessment and
comparison}. In T. Bartz-Beielstein, M. Chiarandini, L. Paquete, and
M. Preuss, editors, Experimental Methods for the Analysis of
Optimization Algorithms, pages 103-130, Springer, Berlin, Germany,
2010.

M. \enc{López-Ibáñez}{Lopez-Ibanez}, L. Paquete, and
T. \enc{Stützle}{Stuetzle}. \emph{Exploratory Analysis of Stochastic Local
Search Algorithms in Biobjective Optimization}. In T. Bartz-Beielstein,
M. Chiarandini, L. Paquete, and M. Preuss, editors, Experimental Methods
for the Analysis of Optimization Algorithms, pages 209–222. Springer,
Berlin, Germany, 2010. doi: 10.1007/978-3-642-02538-9_9
}
\seealso{
Useful links:
\itemize{
  \item \url{http://lopez-ibanez.eu/eaftools}
  \item \url{https://github.com/MLopez-Ibanez/eaf}
  \item Report bugs at \url{https://github.com/MLopez-Ibanez/eaf/issues}
}

}
\author{
Maintainer:  Manuel \enc{López-Ibáñez}{Lopez-Ibanez}
\email{manuel.lopez-ibanez@manchester.ac.uk}

Contributors: Carlos Fonseca, Luis Paquete, Thomas \enc{Stützle}{Stuetzle},
Manuel \enc{López-Ibáñez}{Lopez-Ibanez}, Marco Chiarandini and
\enc{Mickaël}{Mickael} Binois.
}
\concept{empirical attainment function}
\concept{multivariate}
\concept{optimize}
\concept{time-quality algorithm profile}
\keyword{graphs}
\keyword{package}
