eaf (version 1.06)

eaf-package: 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.

encoding

utf8

Details

ll{ Package: eaf Type: Package Version: 1.06 Date: 2014-09-30 Depends: R (>= 2.10.0) Imports: modeltools Suggests: License: GPL (>= 2) URL: http://iridia.ulb.ac.be/~manuel/eaftools LazyLoad: yes }

Functions:

rl{ eafdiffplot Empirical attainment function differences eafplot Plot the Empirical Attainment Function for two objectives read.data.sets Read several data.frame sets }

Data:

[object Object],[object Object],[object Object]

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

ll{

extdata External data sets (see read.data.sets) 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 }

References

V. Grunert da Fonseca, C. M. Fonseca, and A. O. Hall, 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, 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. López-Ibáñez{Lopez-Ibanez}, L. Paquete, and T. Stützle{Stuetzle}. 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

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),include.extremes=TRUE,
	cex=1.4, lty=c(2,1,2),lwd=c(2,2,2),
        col=c("black","blue","grey50"))

A1<-read.data.sets(file.path(system.file(package="eaf"),"extdata","ALG_1_dat"))
A2<-read.data.sets(file.path(system.file(package="eaf"),"extdata","ALG_2_dat"))
eafplot(A1,A2, percentiles=c(50))
eafplot(list(A1=A1, A2=A2), percentiles=c(50))
eafdiffplot(A1, A2)
dev.copy2pdf(file="eaf.pdf", onefile=TRUE, width=5, height=4)