eaf (version 1.9-1)

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.

Details

Functions:

eafdiffplot()	Empirical attainment function differences
eafplot()	Plot the Empirical Attainment Function for two objectives

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

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. L<U+00F3>pez-Ib<U+00E1><U+00F1>ez, L. Paquete, and T. St<U+00FC>tzle. 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<U+2013>222. Springer, Berlin, Germany, 2010. doi: 10.1007/978-3-642-02538-9_9

See Also

Useful links:

• http://lopez-ibanez.eu/eaftools

• https://github.com/MLopez-Ibanez/eaf

• Report bugs at https://github.com/MLopez-Ibanez/eaf/issues

Examples

# NOT RUN {
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)
# }