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Predicting and comparing algorithm performance on graph instances is challenging for multiple reasons. First, there is usually no standard set of instances to benchmark performance. Second, using existing graph generators results in a restricted spectrum of difficulty and the resulting graphs are usually not diverse enough to draw sound conclusions...

## Contexts in source publication

**Context 1**

... brought the total amount of graphs to 24682, comprised of 4500 instances of the first evolutionary algorithm, 20000 instances of the second and third algorithm, and 182 of the standard graph generators. The resulting landscape can be seen in Figure 8. ...

**Context 2**

... landscape of the evolved instances in Figure 8 provides some interesting insights. First, there seem to be distinct areas where all instances in that area have similar differences in runtime. ...

**Context 3**

... of using the difference in runtime to color the instances in the landscape, as was done in Figure 8, we can also use the features of the instances. This can give a better intuition for which instances get solved faster by Concorde or by MSLS. ...

**Context 4**

... is shown in Figure 11, where the density (a), the diameter (b), the standard deviation of the degree distribution (c) and the skewness of the degree distribution (d) are shown. When we compare these figures to the difference in runtime of Figure 8, we see that graphs with a low density are likely to be solved faster by Concorde. There is less correlation with the other features however. ...

**Context 5**

... brought the total amount of graphs to 24682, comprised of 4500 instances of the first evolutionary algorithm, 20000 instances of the second and third algorithm, and 182 of the standard graph generators. The resulting landscape can be seen in Figure 8. ...

**Context 6**

... landscape of the evolved instances in Figure 8 provides some interesting insights. First, there seem to be distinct areas where all instances in that area have similar differences in runtime. ...

**Context 7**

... of using the difference in runtime to color the instances in the landscape, as was done in Figure 8, we can also use the features of the instances. This can give a better intuition for which instances get solved faster by Concorde or by MSLS. ...

**Context 8**

... is shown in Figure 11, where the density (a), the diameter (b), the standard deviation of the degree distribution (c) and the skewness of the degree distribution (d) are shown. When we compare these figures to the difference in runtime of Figure 8, we see that graphs with a low density are likely to be solved faster by Concorde. There is less correlation with the other features however. ...

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