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A Genetic Algorithm for a Large-Scaled Maximal Covering Problem  

박태진 (부산대학교 정보컴퓨터공학부)
황준하 (금오공과대학교 컴퓨터공학)
류광렬 (부산대학교 전자전기정보컴퓨터공학부)
Publication Information
Journal of KIISE:Software and Applications / v.31, no.5, 2004 , pp. 570-576 More about this Journal
Abstract
It is very difficult to efficiently solve a large-scaled maximal covering problem(MCP) by a genetic algorithm. In this paper, we present new crossover and mutation operators specially designed for genetic algorithms to solve large-scaled MCPs efficiently. We also introduce a novel genetic algorithm employing unexpressed genes. Unexpressed genes are the genes which are not expressed and thus do not affect the evaluation of the individuals. These genes play the role of reserving information susceptible to be lost by the application of genetic operations but is suspected to be potentially useful in later generations. The genetic algorithm employing unexpressed genes enjoys the advantage of being able to maintain diversity of the population and thus can search more efficiently to solve large-scaled MCPs. Experiments with large-scaled real MCP data has shown that our genetic algorithm employing unexpressed genes significantly outperforms tabu search which is one of the popularly used local neighborhood search algorithms for optimization.
Keywords
genetic algorithm; maximal covering problem; unexpressed gene;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
연도 인용수 순위
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