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Geometric Crossover for Multiway Graph Partitioning

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Title: Geometric Crossover for Multiway Graph Partitioning


1
Geometric Crossover for Multiway Graph
Partitioning
  • Yong-Hyuk Kim, Yourim Yoon,
  • Alberto Moraglio, and Byung-Ro Moon

2
Contents
  • Multiway graph partitioning
  • Geometric crossover
  • Hamming distance
  • Labeling-independent distance
  • Fitness landscape analysis
  • Experimental results
  • Conclusions

3
Multiway Graph Partitioning Problem
4
Multiway Graph Partitioning
Cut size 5
5
Multiway Graph Partitioning
Cut size 6
6
Geometric Crossover
7
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 .

8
Geometric Crossover
  • The traditional n-point crossover is geometric
    under the Hamming distance.

H(A,X) H(X,B) H(A,B)
9
K-ary encoding and Hamming distance
  • Redundant encoding
  • Hamming distance is not natural.

1 1 2 2 2 3 3
10
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.

11
Labeling-independent Distance
  • A 1213323, B 1122233

1 2 1 3 3 2 3
LI(A,B) 3
12
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.

13
Fitness Landscape Analysis
14
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
15
Normalized correlogram
16
Normalized correlogram
17
Global Convexity
Hamming distance
Correlation coefficient -0.11
18
Global Convexity
Labeling-independent distance
Correlation coefficient 0.79
19
Experimental Results
20
Genetic Framework
  • GA FM variant
  • Population size 50
  • Selection
  • Roulette-wheel proportional selection
  • Replacement
  • Genitor-style replacement
  • Steady-state GA

21
Test Environment
  • Data Set
  • Johnson
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