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A new hybrid approach based on genetic algorithm for minimum vertex cover

机译:一种基于最小顶点盖遗传算法的一种新的混合方法

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Minimum vertex cover (MVC) problem is a NP-Hard optimization problem which we often encounter in real life applications like wireless sensor networks, graph theory, bioinformatics, social network analysis etc. To deal with this optimization problem in an efficient way, we introduce a new Hybrid Genetic algorithm (NHGA) to solve MVC problem. In this study, the proposed algorithm has also been tested on DIMACS benchmarks, BHOSLIB benchmarks and random graphs. Performance of NHGA is then compared with the simple Genetic algorithm (GA) and Clever Greedy algorithm. Consequently, experimental results show that NHGA finds better solutions than other algorithms for MVC, since it offers near to optimal solutions.
机译:最小顶点封面(MVC)问题是NP-Hard优化问题,我们经常在现实生活中遇到无线传感器网络,图论,生物信息学,社交网络分析等,以便以有效的方式处理这种优化问题,我们介绍一种解决MVC问题的新混合遗传算法(NHGA)。在这项研究中,该算法还在Dimacs基准测试中进行了测试,Bhoslib基准和随机图。然后将NHGA的性能与简单的遗传算法(GA)和聪明的贪婪算法进行比较。因此,实验结果表明,NHGA多于MVC的其他算法找到了更好的解决方案,因为它提供靠近最佳解决方案。

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