Figure 8 - uploaded by Thibault Lechien
Content may be subject to copyright.
The filled in landscape of the evolved instances

The filled in landscape of the evolved instances

Source publication
Preprint
Full-text available
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. ...

Similar publications

Article
Full-text available
Multi-view clustering has gained importance in recent times due to the large-scale generation of data, often from multiple sources. Multi-view clustering refers to clustering a set of objects which are expressed by multiple set of features, known as views, such as movies being expressed by the list of actors or by a textual summary of its plot. Co-...
Article
Full-text available
Avoiding conflicting elements is a natural constraint that appears in several graph problems making them more challenging and close to real applications. Minimum Conflict-Free Spanning Tree (MCFST) is a variant of the classic Minimum Spanning Tree (MST) problem, where we are asked to find (if any) the spanning tree avoiding pairs of conflicting edg...
Article
Full-text available
Predicting and comparing algorithm performance on graph instances is challenging for multiple reasons. First, there is not always a standard set of instances to benchmark performance. Second, using existing graph generators results in a restricted spectrum of difficulty and the resulting graphs are not always diverse enough to draw sound conclusion...
Article
Full-text available
Although the performance of hybrid quantum-classical algorithms is highly dependent on the selection of the classical optimizer and the circuit ansätze (Benedetti et al, npj Quantum Inf 5:45, 2019; Hamilton et al, 2018; Zhu et al, 2018), a robust and thorough assessment on-hardware of such features has been missing to date. From the optimizer persp...