Title: Geometric Crossover for Multiway Graph Partitioning
1Geometric Crossover for Multiway Graph
Partitioning
- Yong-Hyuk Kim, Yourim Yoon,
- Alberto Moraglio, and Byung-Ro Moon
2Contents
- Multiway graph partitioning
- Geometric crossover
- Hamming distance
- Labeling-independent distance
- Fitness landscape analysis
- Experimental results
- Conclusions
3Multiway Graph Partitioning Problem
4Multiway Graph Partitioning
Cut size 5
5Multiway Graph Partitioning
Cut size 6
6Geometric Crossover
7Geometric 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 .
8Geometric Crossover
- The traditional n-point crossover is geometric
under the Hamming distance.
H(A,X) H(X,B) H(A,B)
9K-ary encoding and Hamming distance
- Redundant encoding
- Hamming distance is not natural.
1 1 2 2 2 3 3
10Labeling-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.
11Labeling-independent Distance
1 2 1 3 3 2 3
LI(A,B) 3
12N-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.
13Fitness Landscape Analysis
14Distance Distributions
Space E(d)
(all-partition, H) 484.364
(local-optimum, H) 484.369
(all-partition, LI) 429.010
(local-optimum, LI) 274.301
15Normalized correlogram
16Normalized correlogram
17Global Convexity
Hamming distance
Correlation coefficient -0.11
18Global Convexity
Labeling-independent distance
Correlation coefficient 0.79
19Experimental Results
20Genetic Framework
- GA FM variant
- Population size 50
- Selection
- Roulette-wheel proportional selection
- Replacement
- Genitor-style replacement
- Steady-state GA
21Test Environment