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A binary coded multi-parent genetic algorithm for shuttle bus routing system in a college campus

机译:高校校园穿梭巴士路由系统的二进制编码多亲遗传算法

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Genetic algorithm (GA) has been successfully applied for many numerical optimization problems in the history. Multi-parent genetic algorithm (MPGA) is an extended genetic algorithm which uses more than two parent as a crossover operator for reproduction. Since MPGA has been increasing its interest in the family of genetic algorithms, it becomes an interesting algorithm to improve the solutions better than the traditional genetic algorithm by using the number of parents more than two for solving shuttle bus routing system (SBRS). In this paper, we compare MPGA and the traditional GA for the problem of SBRS in the Thammasat University (Rangsit Campus), Thailand. MPGA with up to 20 parents are used to optimize the shuttle bus routes in the campus. The diagonal crossover is used to measure the performance for both MPGA and GA in the reproduction process. The results prove that using multiple parents yields better solution than the traditional GA for solving the problem of SBRS.
机译:遗传算法(GA)已成功应用于历史上的许多数值优化问题。多亲遗传算法(MPGA)是一种扩展遗传算法,它使用两个以上的父代作为交叉算子进行繁殖。由于MPGA对遗传算法家族的兴趣不断增加,通过使用多于两个的父代数来解决穿梭巴士路由系统(SBRS),它成为一种比传统遗传算法更好地改进解决方案的有趣算法。在本文中,我们将MPGA和传统GA进行了比较,比较了泰国Thammasat大学(Rangsit校园)的SBRS问题。 MPGA(最多可容纳20名家长)用于优化校园中的班车路线。对角交叉用于测量再现过程中MPGA和GA的性能。结果证明,与传统遗传算法相比,使用多亲可以更好地解决SBRS问题。

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