geometric crossover for multiway graph partitioning yong-hyuk kim, yourim yoon, alberto moraglio,...
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Geometric Crossover for Multiway Graph Partitioning
Yong-Hyuk Kim, Yourim Yoon,
Alberto Moraglio, and Byung-Ro Moon
Contents
• Multiway graph partitioning
• Geometric crossover– Hamming distance– Labeling-independent distance
• Fitness landscape analysis
• Experimental results
• Conclusions
Geometric Crossover
• Line segment
• A binary operator GX is a geometric crossover if all offspring are in a segment between its parents.
• Geometric crossover is dependent on the metric .
x y
Geometric Crossover
• The traditional n-point crossover is geometric under the Hamming distance.
10110
11011
A
B
A
B
11010X
X2
1
3
H(A,X) + H(X,B) = H(A,B)
K-ary encoding and Hamming distance
• Redundant encoding– Hamming distance is not natural.
1
2
4
6
7
35
1 1 2 2 2 3 3
2233311
3311122
2211133
1122233
1133322
6 different representations
Labeling-independent Distance
• Given two K-ary encoding, and ,
,
where is a metric.
• If the metric is the Hamming distance H, LI can be computed efficiently by the Hungarian method.
Labeling-independent Distance
• A = 1213323, B = 1122233
3322211
2233311
3311122
2211133
1122233
1133322
1 2 1 3 3 2 3 5
4
3
5
4
7
LI(A,B) = 3
N-point LI-GX
• Definition (N-point LI-GX)– Normalize the second parent to the first
under the Hamming distance. Do the normal n-point crossover using the first parent and the normalized second parent.
• The n-point LI-GX is geometric under the labeling-independent metric.
Distance Distributions
Space E(d)
(all-partition, H) 484.364
(local-optimum, H) 484.369
(all-partition, LI) 429.010
(local-optimum, LI) 274.301
Genetic Framework
• GA + FM variant
• Population size : 50
• Selection– Roulette-wheel proportional selection
• Replacement– Genitor-style replacement
• Steady-state GA
Test Environment
• Data Set– Johnson’s benchmark data– 4 random graphs (G*.*) and 4 random
geometric graphs (U*.*) with 500 vertices.– Used in a number of other graph-
partitioning studies.
• Tests on 32-way and 128-way partitioning
Experimental Results
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
G500.2.5 G500.05 G500.10 G500.20
5pt H-GX GEFM 5pt LI-GX
32-way partitioning
Experimental Results
32-way partitioning
0
1
2
3
4
5
6
7
8
9
U500.05 U500.10 U500.20 U500.40
5pt H-GX GEFM 5pt LI-GX
Experimental Results
128-way partitioning
0
0.5
1
1.5
2
2.5
3
3.5
G500.2.5 G500.05 G500.10 G500.20
5pt H-GX GEFM 5pt LI-GX
Experimental Results
0
0.2
0.4
0.6
0.8
1
1.2
1.4
U500.05 U500.10 U500.20 U500.40
5pt H-GX GEFM 5pt LI-GX
128-way partitioning
Conclusion
• Methodology– Designed a geometric crossover based on th
e labeling independent distance.– Provided evidence for the fact that the labelin
g-independent distance is more suitable for the multiway graph partitioning problem by the fitness landscape analysis.
• Performance– Performed better than existing genetic algorit
hms.