eaf-package: Computation and visualization of the empirical attainment...

Description Functions Data Author(s) References See Also Examples

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.

Functions

eafdiffplot() Empirical attainment function differences
eafplot() Plot the Empirical Attainment Function for two objectives
read_datasets() Read several data.frame sets

Data

gcp2x2

Metaheuristics for solving the Graph Vertex Coloring Problem

HybridGA

Results of Hybrid GA on vanzyl and Richmond water networks

SPEA2minstoptimeRichmond

Results of SPEA2 when minimising electrical cost and maximising the minimum idle time of pumps on Richmond water network

Extras are available at system.file(package="eaf"):

extdata External data sets (see read_datasets)
scripts/eaf EAF command-line program
scripts/eafplot Perl script to generate plots of attainment surfaces
scripts/eafdiff Perl script to generate plots of EAF differences

Author(s)

Maintainer: Manuel López-Ibáñez manuel.lopez-ibanez@manchester.ac.uk

Contributors: Carlos Fonseca, Luis Paquete, Thomas Stützle, Manuel López-Ibáñez, Marco Chiarandini and Mickaël Binois.

References

\insertRef

Grunert01eaf

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GruFon2009:emaaeaf

\insertRef

LopPaqStu09emaaeaf

See Also

Useful links:

Examples

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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.xz"))
A2 <- read_datasets(file.path(extdata_path,"ALG_2_dat.xz"))
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)

eaf documentation built on May 7, 2021, 5:06 p.m.