Title
Optimisation of control parameters for genetic algorithms to test computer networks under realistic traffic loads
Abstract
Although many studies have focused on testing computer networks under realistic traffic loads by means of genetic algorithms (GAs), little attention has been paid to optimising the parameters of the GAs in this problem. The objective of this work is to design and validate a system that, given some constraints on traffic bandwidth, generates the worst-case traffic for a given computer network and finds the traffic configuration (critical background traffic) that minimises throughput in that computer network. The proposed system is based on a meta-GA, which is combined with an adaptation strategy that finds the optimum values for the GA control parameters and adjusts them to improve the GA's performance. To validate the approach, different comparisons are performed with the goal of assessing the acceptable optimisation power of the proposed system. Moreover, a statistical analysis was conducted to ascertain whether differences between the proposed system and other algorithms are significant. The results demonstrate the effectiveness of the system and prove that, when the background traffic is driven by a GA that uses the parameters obtained from the system, the computer network's performance is much lower than when the traffic is generated by Poisson statistical processes or by other algorithms. This system has identified the worst traffic pattern for the protocol under analysis.
Year
DOI
Venue
2012
10.1016/j.asoc.2011.02.004
Appl. Soft Comput.
Keywords
DocType
Volume
critical background traffic,genetic algorithm,realistic traffic loads,worst traffic pattern,traffic bandwidth,computer network,background traffic,realistic traffic load,worst-case traffic,computer networks,realistic traffic,ga control parameter,parameter control,proposed system,traffic configuration,statistical analysis
Journal
11
Issue
ISSN
Citations 
4
Applied Soft Computing Journal
5
PageRank 
References 
Authors
0.46
21
4