The scale at which the human race consumes data has increased exponentially in recent years. One key part in this increase has been the usage of smart phones and connected devices by the populous. Multi-level heterogeneous networks are the driving force behind this mobile revolution, but these are constrained with limited bandwidth and over-subscription. Scheduling users on these networks has become a growing issue. In recent years grammar-guided genetic programming (G3P) has shown its capability to evolve beyond human-competitive network schedulers. Despite the performance of the G3P schedulers, a large margin of improvement is demonstrated to still exist. In the pursuit of this goal we recently proposed a multi-level grammar approach to generating schedulers. The complexity of the grammar was increased at various stages during evolution, allowing for individuals to add more complex functions through variation operations. The goal is to evolve good quality solutions before allowing the population to specialise more as the grammar functionality increased in a layered learning way. In this paper the results of this initial study are replicated, and confirmed, and it is seen that this approach improves the quality of the evolved schedulers. However, despite the gain in performance, we notice that the proposed approach comes with an acute sensitivity to the generation at which the grammar complexity is increased. Therefore, we put forward a novel seeding strategy and show that the seeding strategy mitigates the shortcomings of the original approach. The use of the seeding strategy outperforms the original approach in all the studied cases, and thus yields a better overall performance than the state-of-the-art G3P generated schedulers.

中文翻译：

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一种语法引导的遗传编程的多级语法方法：异构网络中的调度案例

近年来，人类消费数据的规模呈指数级增长。这一增长的一个关键部分是人口众多的智能手机和连接设备的使用。多层次异构网络是这场移动革命背后的驱动力，但这些网络受到有限带宽和超额订阅的限制。在这些网络上调度用户已成为一个日益严重的问题。近年来，语法引导的遗传编程 (G3P) 已经显示出其超越人类竞争网络调度程序的能力。尽管 G3P 调度器的性能有所提高，但仍有很大的改进余地。为了实现这个目标，我们最近提出了一种生成调度程序的多级语法方法。语法的复杂性在进化的各个阶段都有所增加，允许个人通过变异操作添加更复杂的功能。随着语法功能以分层学习的方式增加，我们的目标是在允许人群更加专业化之前开发出高质量的解决方案。在本文中，这一初步研究的结果被复制和确认，可以看出这种方法提高了进化调度程序的质量。然而，尽管性能有所提高，我们注意到所提出的方法对语法复杂性增加的生成具有敏锐的敏感性。因此，我们提出了一种新的播种策略，并表明该播种策略减轻了原始方法的缺点。在所有研究案例中，播种策略的使用优于原始方法，