Plot the differences between the empirical attainment functions of two data sets as a two-panel plot, where the left side shows the values of the left EAF minus the right EAF and the right side shows the differences in the other direction.
Computes and plots the Empirical Attainment Function, either as attainment surfaces for certain percentiles or as points. This function can be used to plot random sets of points like those obtained by different runs of biobjective stochastic optimization algorithms. An EAF curve represents the boundary separating points that are known to be attainable (that is, dominated in Pareto sense) in at least a fraction (quantile) of the runs from those that are not. The median EAF represents the curve where the fraction of attainable points is 50%. In single objective optimization the function can be used to plot the profile of solution quality over time of a collection of runs of a stochastic optmizer.
Reads a text file in table format and creates a data frame from it. The file may contain several sets, separated by empty lines. The function adds an additional column set to indicate to which set each row belongs.
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.
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