Paper Info
Title
Genetic approaches for graph partitioning: a survey
Abstract
The graph partitioning problem occurs in numerous applications such as circuit placement, matrix factorization, load balancing, and community detection. For this problem, genetic algorithm is a representative approach with competitive performance with many related papers being published. Although there are a number of surveys on graph partitioning, none of them deals with genetic algorithms in much detail. In this survey, a number of problem-specific issues in applying genetic algorithms to the graph partitioning problem are discussed; the issues include encoding, crossover, normalization, and balancing.
Year
DOI
Venue
2011
10.1145/2001576.2001642
GECCO
Keywords
Field
DocType
genetic approach,problem-specific issue,load balancing,related paper,genetic algorithm,matrix factorization,competitive performance,numerous application,graph partitioning,circuit placement,community detection,load balance,combinatorial optimization,genetics
Computer science,Theoretical computer science,Artificial intelligence,Graph bandwidth,Graph partition,Genetic algorithm,Mathematical optimization,Crossover,Load balancing (computing),Matrix decomposition,Combinatorial optimization,Machine learning,Graph (abstract data type)
Conference
Citations 
PageRank 
References 
25
0.96
53
Authors
4
Name
Order
Citations
PageRank
Jin Kim1374.95
Inwook Hwang29911.11
Yong-Hyuk Kim335540.27
Byung-Ro Moon484458.71