Genetic approaches for graph partitioning: a survey

Authors:
Jin Kim;Inwook Hwang;Yong-Hyuk Kim;Byung-Ro Moon
Affiliations:
Seoul National University, Seoul, South Korea;Samsung Electronics, Gyeonggi-do, South Korea;Kwangwoon University, Seoul, South Korea;Seoul National University, Seoul, South Korea
Venue:
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Year:
2011

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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.