Performance of a genetic algorithm for the graph partitioning problem

Authors:
Keiko Kohmoto;Kengo Katayama;Hiroyuki Narihisa
Affiliations:
Department of Distribution Engineering Hiroshima National College of Maritime Technology Hiroshima, Japan;Department of Information and Computer Engineering Okayama University of Science Okayama, Japan;Department of Information and Computer Engineering Okayama University of Science Okayama, Japan
Venue:
Mathematical and Computer Modelling: An International Journal
Year:
2003

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Abstract

The performance of the genetic algorithm (GA) for the graph partitioning problem (GPP) is investigated by comparison with standard heuristics on well-known benchmark graphs. In general, there is a case where a practical performance of a conventional genetic approach, which performs only simple operations without a local search strategy, is not sufficient. However, it is known that a combination of GA and local search can produce better solutions. From this practice, we incorporate a simple local search algorithm into the GA. In particular, the search ability of the GA is compared with standard heuristics such as multistart local search and simulated annealing, which use the same neighborhood structure of the simple local search, for solving the GPP. Experimental results show that the GA performs better than its competitors.