Overview of performance values

The following statistics were calculated from the performance values of each algorithm:

obs (number of observations within performance values)
nas (number of NAs, i.e., missing values, within performance values)
min (minimum), mean (arithmetic mean), max (maximum), sd (standard deviation)
qu_1st (1st quartile = lower quartile = 25%-quantile)
med (median = 50%-quantile)
qu_3rd (3rd quartile = upper quartile = 75%-quantile)
coeff_var (coefficient of variation = standard deviation / arithmetic mean)

	obs	min	qu_1st	med	mean	qu_3rd	max	sd	coeff_var
astar.symmulgt.transmul	527	0.012	1.176	46.627	1296.31	3600	3600	1671.52	1.28945
astar.symmullt.transmul	527	0.016	1.69	85.521	1387.76	3600	3600	1697.9	1.22349
idastar.symmulgt.transmul	527	0	0.544	49.899	916.379	1255.42	3600	1424.28	1.55425
idastar.symmullt.transmul	527	0.004	1.888	81.685	1059.27	2424.96	3600	1478.77	1.39602

Summary of the runstatus per algorithm

The following table summarizes the runstatus of each algorithm over all instances (in %).

	ok	timeout	memout
astar.symmulgt.transmul	66.224	0.000	33.776
astar.symmullt.transmul	63.378	0.000	36.622
idastar.symmulgt.transmul	81.214	18.786	0.000
idastar.symmullt.transmul	78.748	21.252	0.000

Dominated Algorithms

Here, you'll find an overview of dominating/dominated algorithms:
None of the algorithms was superior to any of the other.

An algorithm (A) is considered to be superior to an other algorithm (B), if it has at least an equal performance on all instances (compared to B) and if it is better on at least one of them. A missing value is automatically a worse performance. However, instances which could not be solved by either one of the algorithms, were not considered for the dominance relation.

Visualisations

Important note w.r.t. some of the following plots:
If appropriate, we imputed performance values for failed or censored runs. We used max + 0.3 * (max - min), in case of minimization problems, or min - 0.3 * (max - min), in case of maximization problems.
In addition, a small noise is added to the imputed values (except for the cluster matrix, based on correlations, which is shown at the end of this page).

Boxplots of performance values

Imputing the performance values of failed or censored runs (as described in the red note at the beginning of this section):

Discarding the performance values of failed or censored runs:

## Warning: Removed 582 rows containing non-finite values (stat_boxplot).

Estimated densitities of performance values

Imputing the performance values of failed or censored runs (as described in the red note at the beginning of this section):

Discarding the performance values of failed or censored runs:

Estimated cumulative distribution functions of performance values

Imputing the performance values of failed runs (as described in the red note at the beginning of this section):

Discarding the performance values of failed or censored runs:

Scatterplot matrix of the performance values

The figure underneath shows pairwise scatterplots of the performance values.

Imputing the performance values of failed and censored runs (as described in the red note at the beginning of this section):

Clustering algorithms based on their correlations

The following figure shows the correlations of the ranks of the performance values. Per default it will show the correlation coefficient of spearman. Missing values were imputed prior to computing the correlation coefficients. The algorithms are ordered in a way that similar (highly correlated) algorithms are close to each other. Per default the clustering is based on hierarchical clustering, using Ward's method.