Paper Info
Title
A multi-level grammar approach to grammar-guided genetic programming: the case of scheduling in heterogeneous networks.
Abstract
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.
Year
DOI
Venue
2019
10.1007/s10710-019-09346-4
Genetic Programming and Evolvable Machines
Keywords
Field
DocType
Telecommunication, Heterogeneous network, Scheduling, Grammar-guided genetic programming, Multi-level grammar, Seeding
Population,Scheduling (computing),Computer science,Genetic programming,Grammar,Layered learning,Bandwidth (signal processing),Notice,Artificial intelligence,Heterogeneous network,Machine learning,Distributed computing
Journal
Volume
Issue
ISSN
20
2
1389-2576
Citations 
PageRank 
References 
0
0.34
37
Authors
6
Name
Order
Citations
PageRank
Takfarinas Saber1254.90
David Fagan2499.20
David Lynch382.85
Stepan Kucera49019.50
Holger Claussen51575183.63
Michael O'Neill687669.58